ai to read research papers

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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)

As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change. 

Make no mistake, AI is here to stay!

Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.

These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!

This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.

I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!

Here is everything you need to know about AI for academic research and the ones I have personally trialed on my YouTube channel.

My Top AI Tools for Researchers and Academics – Tested and Reviewed!

There are many different tools now available on the market but there are only a handful that are specifically designed with researchers and academics as their primary user.

These are my recommendations that’ll cover almost everything that you’ll want to do:

Want to find out all of the tools that you could use?

Here they are, below:

AI literature search and mapping – best AI tools for a literature review – elicit and more

Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!

AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.

They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.

With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.

• Elicit –  https://elicit.org
• Litmaps –  https://www.litmaps.com
• Research rabbit – https://www.researchrabbit.ai/
• Connected Papers –  https://www.connectedpapers.com/
• Supersymmetry.ai: https://www.supersymmetry.ai
• Semantic Scholar: https://www.semanticscholar.org
• Laser AI –  https://laser.ai/
• Inciteful –  https://inciteful.xyz/
• Scite –  https://scite.ai/
• System –  https://www.system.com

If you like AI tools you may want to check out this article:

• How to get ChatGPT to write an essay [The prompts you need]

AI-powered research tools and AI for academic research

AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research. 

These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from. 

Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.

The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!

• Consensus –  https://consensus.app/
• Iris AI –  https://iris.ai/
• Research Buddy –  https://researchbuddy.app/
• Mirror Think – https://mirrorthink.ai

AI for reading peer-reviewed papers easily

Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in. 

These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!

They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.

With AI, deciphering complex citations and accelerating research has never been easier.

• Aetherbrain – https://aetherbrain.ai
• Explain Paper – https://www.explainpaper.com
• Chat PDF – https://www.chatpdf.com
• Humata – https://www.humata.ai/
• Lateral AI –  https://www.lateral.io/
• Paper Brain –  https://www.paperbrain.study/
• Scholarcy – https://www.scholarcy.com/
• SciSpace Copilot –  https://typeset.io/
• Unriddle – https://www.unriddle.ai/
• Sharly.ai – https://www.sharly.ai/
• Open Read –  https://www.openread.academy

AI for scientific writing and research papers

In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.

Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.

Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.

• Jenny.AI – https://jenni.ai/ (20% off with code ANDY20)
• Yomu – https://www.yomu.ai
• Wisio – https://www.wisio.app

AI academic editing tools

In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.

Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.

• PaperPal –  https://paperpal.com/
• Writefull –  https://www.writefull.com/
• Trinka –  https://www.trinka.ai/

AI tools for grant writing

In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.

These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.

Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.

Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.

• Granted AI – https://grantedai.com/
• Grantable – https://grantable.co/

Best free AI research tools

There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.

The best free ones at time of writing are:

• Elicit – https://elicit.org
• Connected Papers – https://www.connectedpapers.com/
• Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
• Consensus – https://consensus.app/

Wrapping up

The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.

With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.

The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.

These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.

They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.

Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.

And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.

We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.

Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!

Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

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6 Best AI tools for Reading Research Papers

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• 【Tag】 ai read papers , ai text generation , ai writing essay , essay Generator

The advent of AI has also impacted academia. Researchers and scientists no longer need to spend extensive time reading literature and large volumes of data. AI can read papers, perform tasks, and organize extensive literature, eliminating the tedious processes of reading and analysis, thus allowing quicker acquisition of knowledge.

Table of Contents

What AI tools are available for research?

Many researchers often struggle with the overwhelming task of reading and synthesizing data, unsure where to start or how to efficiently organize and analyze literature. AI tools for reading papers enable students and researchers to swiftly and accurately summarize and analyze documents. Here are some recommended AI tools for research :

GenApe AI is an advanced tool for generating text and images. It offers various AI assistants for academic papers, such as AI summarizers, title generators, and descriptive tools. Notably, the AI paper reader we are introducing swiftly analyzes documents and allows you to pose questions based on your specific needs.

I am using the Chatbot Ape from the menu. First, click on “Analyze Document” and upload your file. Next, you can specify your needs, whether you want an outline of the document or a summary.

I chose to have it summarize the document. The overall usage is simple and user-friendly. Unlike other AI tools for reading papers, it allows you to customize the style. The chatbot can provide answers tailored to your needs.

Additionally, for more recent information, you can enable real-time web access, ensuring that the information is accurate and up-to-date.

Immediately avail a complimentary trial without credit card required： https://app.genape.ai/chatApe

Explainpaper

I offer rapid academic paper analysis services utilizing artificial intelligence technology primarily for English language texts. I provide explanations for complex vocabulary found within papers upon highlighting.

Additionally, detailed abstract analysis is available, along with an AI chat feature for querying research-related questions and comprehending intricate content within papers or literature, aiding in a clearer understanding.

The usage is straightforward. After registration, a simple interface will appear. You can upload a file or paste a link to have the file read for you. Then, the tools mentioned earlier will appear in the right-hand column.

I tested a 94-page PDF file here, but it didn’t generate results, possibly due to limitations of the free account or the file’s size. Processing may require additional time. Asking AI questions incurs charges, but overall, it’s user-friendly for beginners and doesn’t require extensive time for writing papers.

ChatPDF is a paper reading tool based on the GPT-3 language model. By uploading a paper PDF, ChatPDF utilizes AI to analyze the paper. On the left side, you’ll see the paper itself, and on the right side, there is an AI chat interface.

It provides prompts on how to ask questions effectively, allowing you to quickly view key points of the paper or extract important keywords. Essentially, the free version of ChatPDF offers sufficient functionality for most users.

I have tested the same paper above and asked him to summarize the abstract for me. He was able to respond quickly. In the chat part, he will answer in Chinese if asked in Chinese and in English if asked in English.

It’s a rather intelligent chatroom that can quickly help you understand this paper. Another advantage is that you can use it without registering.

SciSpace Copilot

SciSpace Copilot is an AI tool designed specifically to enhance the comprehension of academic literature. It offers both a web-based version and an extension, making it accessible and user-friendly. Simply upload your PDF file, and it will identify relevant papers, providing functions for semantic analysis and scholarly discourse.

Once you upload your paper, you’ll see the Copilot chat on the right. It will suggest actions closely related to your paper, such as summarizing and analyzing it. Just follow the prompts, and it will swiftly assist you in analysis.

The analysis results will appear as shown in the diagram below. While he can choose the language, currently only Simplified Chinese is supported. Of course, there are other language options available for selection.

In addition to analyzing the results, it is also possible to quickly summarize a particular segment of literature. Simply select the content on the left and press the “Explain Text” button to obtain the conclusion.

Scholarcy is an AI academic paper abstract tool developed by the UK-based company Cactus Communications. It assists researchers in effortlessly locating, organizing, and comprehending scholarly literature.

Scholarcy utilizes natural language processing to automatically extract information from academic papers and transform it into concise, easily comprehensible abstracts.

Scholarcy requires registration before use. Unlike other AI tools for reading papers, after uploading a file, it allows customization of summaries, such as adjusting the word count or selecting key points, to tailor the summary according to personal preferences.

After analysis, the result will appear as shown in the diagram below, listing numerous items, although some internal content may be null.

Take a look at one example: I find Scholarcy more challenging to use overall compared to other analytical tools, with a more complex operational interface as well.

AI reading thesis extended reading： 5 Best AI Summarizing Tool , Text summarizer

Advantages of AI for reading papers

The main advantages of using AI to read essays are as follows:

Quickly capture the key points of a document

AI’s ability to read papers enables effortless extraction of key points from literature. Compared to manual reading and analysis, AI can swiftly identify content within documents, offering concise summaries and analyses, thereby saving considerable time.

Simplify the complexity

The terminology of academic papers is typically specialized and the content can be lengthy. Using AI to summarize papers reduces the content, making it easier and more efficient for you to comprehend literature and academic papers.

Easy Data Extraction

AI’s ability to read papers can assist in extracting critical information, enabling you to swiftly locate the necessary data.

How to effectively read academic papers?

In addition to using AI for reading papers, there are also techniques that can enhance your efficiency in paper reading. Here are tips to help you improve your effectiveness in reading academic papers:

Start with the Summary and Introduction

Whether you seek understanding of a new topic or wish to conduct research, when reading academic papers, you should begin with the abstract and introduction. The abstract provides an overview of the paper as a whole, aiding in quickly determining its relevance to your research topic.

Meanwhile, the introduction explains the research problem and its significance, allowing you to grasp the background and objectives of the study beforehand.

View Structure

Then, you should check the table of contents of this paper and pay attention to the main parts of the paper, such as introduction, methodology, results and discussion, etc. This will help you to find the part you need quickly and save unnecessary reading time.

Focus on Results and Discussions

When you are sure that the paper will be useful for your research, you can read it in depth for the sections you want to check out, mostly looking at the Results and Discussion sections, which are the 2 sections that will allow you to quickly grasp the core conclusions of the research.

AI reads papers easily and saves time

AI reading tools can enhance the ability to write essays, recommend GenApe AI tools, in addition to the above mentioned analysis of thesis, for academic research has been trained to use a variety of AI assistants, as long as you enter the content you want to quickly have a conclusion, outline, title, and of course, can also be analyzed in the paper, and now do not need to credit card can be free to try it out, hurry up and click on the button below!

Let’s start free trial today!

Join now and start changing the way you write！

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Analyze research papers at superhuman speed

Search for research papers, get one sentence abstract summaries, select relevant papers and search for more like them, extract details from papers into an organized table.

Find themes and concepts across many papers

Don't just take our word for it.

Tons of features to speed up your research

Upload your own pdfs, orient with a quick summary, view sources for every answer, ask questions to papers, research for the machine intelligence age, pick a plan that's right for you, get in touch, enterprise and institutions, common questions. great answers., how do researchers use elicit.

Over 2 million researchers have used Elicit. Researchers commonly use Elicit to:

• Speed up literature review
• Find papers they couldn’t find elsewhere
• Automate systematic reviews and meta-analyses
• Learn about a new domain

Elicit tends to work best for empirical domains that involve experiments and concrete results. This type of research is common in biomedicine and machine learning.

What is Elicit not a good fit for?

Elicit does not currently answer questions or surface information that is not written about in an academic paper. It tends to work less well for identifying facts (e.g. "How many cars were sold in Malaysia last year?") and in theoretical or non-empirical domains.

What types of data can Elicit search over?

Elicit searches across 125 million academic papers from the Semantic Scholar corpus, which covers all academic disciplines. When you extract data from papers in Elicit, Elicit will use the full text if available or the abstract if not.

How accurate are the answers in Elicit?

A good rule of thumb is to assume that around 90% of the information you see in Elicit is accurate. While we do our best to increase accuracy without skyrocketing costs, it’s very important for you to check the work in Elicit closely. We try to make this easier for you by identifying all of the sources for information generated with language models.

How can you get in contact with the team?

You can email us at [email protected] or post in our Slack community ! We log and incorporate all user comments, and will do our best to reply to every inquiry as soon as possible.

What happens to papers uploaded to Elicit?

When you upload papers to analyze in Elicit, those papers will remain private to you and will not be shared with anyone else.

How accurate is Elicit?

Training our models on specific tasks, searching over academic papers, making it easy to double-check answers, save time, think more. try elicit for free..

RAxter is now Enago Read! Enjoy the same licensing and pricing with enhanced capabilities. No action required for existing customers.

Your all in one AI-powered Reading Assistant

A Reading Space to Ideate, Create Knowledge, and Collaborate on Your Research

• Smartly organize your research
• Receive recommendations that cannot be ignored
• Collaborate with your team to read, discuss, and share knowledge

From Surface-Level Exploration to Critical Reading - All in one Place!

Fine-tune your literature search.

Our AI-powered reading assistant saves time spent on the exploration of relevant resources and allows you to focus more on reading.

Select phrases or specific sections and explore more research papers related to the core aspects of your selections. Pin the useful ones for future references.

Our platform brings you the latest research related to your and project work.

Speed up your literature review

Quickly generate a summary of key sections of any paper with our summarizer.

Make informed decisions about which papers are relevant, and where to invest your time in further reading.

Get key insights from the paper, quickly comprehend the paper’s unique approach, and recall the key points.

Bring order to your research projects

Organize your reading lists into different projects and maintain the context of your research.

Quickly sort items into collections and tag or filter them according to keywords and color codes.

Experience the power of sharing by finding all the shared literature at one place.

Decode papers effortlessly for faster comprehension

Highlight what is important so that you can retrieve it faster next time.

Select any text in the paper and ask Copilot to explain it to help you get a deeper understanding.

Ask questions and follow-ups from AI-powered Copilot.

Collaborate to read with your team, professors, or students

Share and discuss literature and drafts with your study group, colleagues, experts, and advisors. Recommend valuable resources and help each other for better understanding.

Work in shared projects efficiently and improve visibility within your study group or lab members.

Keep track of your team's progress by being constantly connected and engaging in active knowledge transfer by requesting full access to relevant papers and drafts.

Find papers from across the world's largest repositories

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Everything you add or create on Enago Read is private by default. It is visible if and when you share it with other users.

You can put Creative Commons license on original drafts to protect your IP. For shared files, Enago Read always maintains a copy in case of deletion by collaborators or revoked access.

We use state-of-the-art security protocols and algorithms including MD5 Encryption, SSL, and HTTPS to secure your data.

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Introducing ChatPDF: Your AI assistant that helps explain papers

Here's one thing most of us agree on, processing information in research papers takes work. But wouldn't it be great if you could have AI explain papers to you no matter what the topic is, as you read, whenever you want?

Picture this: every time you see a complicated equation in research papers, you’d get an explanation of it right there on the same screen. Even better, imagine you can ask any number of follow-up questions and get answers to them instantly. That's exactly what you get with SciSpace ChatPDF !

Use this ChatPDF to get explanations and answers on any research paper as you read. Works for tables, equations, diagrams, jargon, and even lengthy blocks of text. You don't have to pause and search for it elsewhere. And your learning flow won't be disrupted.

Critical thinking and problem-solving are the bedrock of research. ChatPDF provides all the contextual information you ask for in real-time. It gives you more time to think about the research and focus more on making inferences and drawing meaningful conclusions.

What is SciSpace ChatPDF?

ChatPDF is like your own personal research assistant. It is powered by artificial intelligence (AI), and sits on top of our research repository .

Whenever you need help, the AI research assistant is there to explain the paper, answer your queries, and provide you with the context you need within the same page.

How to use SciSpace ChatPDF to explain papers?

For starters, the AI assistant is available across all the 200 million+ papers on the SciSpace repository. So, you can simply search for the paper or topic you want to look up to get started.

Or, if you have the PDF stored on your device, you can sign up to SciSpace and then upload the same. Either way, ChatPDF explains papers and provides the answers you need.

It can assist you whether you're working on your literature review, catching up on the latest in your field, or just reading for fun.

Let's look at how SciSpace ChatPDF or SciSpace Copilot explains papers and helps you to read, break down, and understand them effectively.

1. Highlight text to understand them better

Came across unfamiliar terms or acronyms while reading a research paper? With ChatPDF, simply highlight it to get an explanation on the same screen.

It works for paragraphs too. So, next time you're stuck while reading a paper, just select the text which requires further elaboration. Get background information about what is being discussed in the passage — concepts, theories, methods and learn how they are relevant to the paper.

Use the summarize feature to get a condensed version of lengthy paragraphs, helping you grasp the main points quickly and efficiently. Employ the same feature to quickly figure out if a paper is worth reading.

2. Crop formulas and tables to learn their implications

Comprehending the math in a paper can be challenging. You can skim through and read the results, but what if there was a better option?

Now just clip every equation you see in a paper to get ChatPDF to explain the math to you. Glean more insights by breaking equations down step-by-step and making sense of their implications.

You can also crop tables for an overview of the data. It should help you analyze and examine the data more closely and gain more context into the conclusions drawn by the author.

3. Ask questions to get more context and clarity

Learning cannot be complete without questions. Asking questions is how you connect your existing knowledge with new information. You can, of course, refer to another text or reach out to authors or peers with your queries. But what if you need a quick answer so that you can keep reading?

ChatPDF makes that easy and instant. Just type in your query as you're reading, and the AI research assistant provides a relevant answer on the same page. Be it a technical question or something related to the theory or methodology. Ask any number of questions regarding the research paper.

On top of that, if ChatPDF's initial answer to a question fails to clarify your doubt completely, you can zero in on it with follow-up questions. You can also do the same if you want to dig deeper into the explanations you receive for excerpts and equations.

If you are unsure what to ask, ChatPDF provides preset questions for you to explore the paper. These pre-generated questions cover various aspects of the research and are designed to help you understand the topic comprehensively.

Besides generic questions like "What is the research question?" or "What are the key findings?", you'll get specific questions drawn from the paper you're reading. These questions are tailor-made to address key points in the research, facilitating a deeper understanding and encouraging critical thinking.

These are the three key ways you can use ChatPDF. In addition to this, you can converse with the AI research assistant in multiple languages. It can explain papers and provide answers in any language you choose. We currently support 75+ languages and plan to add many more.

And please know that your conversation with ChatPDF on a particular paper is automatically saved. This way, you can refer back to it anytime you need.

New additions to SciSpace ChatPDF

Since we released ChatPDF (formerly SciSpace Copilot) in November 2022, we have added many new features to improve your research reading workflow.

Here are some of the key changes:

Now connect your SciSpace Library with your Zotero Library. You can directly import PDFs of any research paper, conference proceeding, or preprint into your SciSpace Library and use ChatPDF to gain a deeper understanding.

You can also highlight key sections and add notes to the PDF as you read. And top it off by turning helpful explanations and answers from ChatPDF into notes — making it easy to review the paper in the future.

SciSpace ChatPDF is now available as a Chrome extension . It works everywhere, from Elsevier and Medium to YouTube.

New SciSpace ChatPDF (formerly Copilot) feature update

In September 2023, we added a citation interlinking feature to ensure the answers Copilot generates are from an original source.

What is citation interlinking feature?

Now, SciSpace Copilot gives a citation backed answer to your every query!

First select the focus by clicking the '+' icon at the bottom left. Then, choose the source from which Copilot should fetch information.

• "Only this paper" — To get answers from your current PDF.
• "All SciSpace papers" — To get answers from our 270M+ papers.
• "My Library" — To get answers from your own PDF collection.

Next step is to ask a question to the Copilot about the paper you are reading.

Every answer the Copilot provides includes citations of the PDF the answer is extracted from. You can click the citation and see the exact section of the PDF the answer is extracted from.

By providing direct citations, you can immediately verify the accuracy and relevance of the answer, ensuring that the AI hasn't just generated a random or inaccurate response.

You can use SciSpace ResearchGPT – your new go-to tool for exploring research effortlessly!

Wrapping up

Copilot is still very much a work in progress. We are continuously working to enhance the features and make Copilot even more helpful for researchers and other research readers. The aim is to make research papers more interactive so that you get contextual help while reading.

We at SciSpace are working to make every published paper utilized to its optimum. Copilot is just the beginning; join us on our journey.

We’d love you to try it out and tell us about your experience. You can join our Discord Community , write to us on Twitter , or email us at [email protected] .

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Using AI for research: A beginner’s guide

The Semantic Reader Open Research Platform

Semantic Reader Project is a collaborative effort of NLP + HCI researchers from non-profit, industry, and academic institutions to create interactive, intelligent reading interfaces for scholarly papers. Our research led to the creation of Semantic Reader, an application used by tens of thousands of scholars each week.

The Semantic Reader Open Research Platform provides resources that enable the broader research community to explore exciting challenges around novel research support tools: PaperMage , a library for processing and analyzing scholarly PDFs, and PaperCraft , a React UI component for building augmented and interactive reading interfaces. Join us in designing the future of scholarly reading interfaces with our open source libraries!

Open Source Libraries

We provide PaperMage + PaperCraft for building intelligent and interactive paper readers. Below we showcase how to extract text from a PDF to prompt a LLM for term definitions and then visually augment the paper with highlights and popups.

Process and Analyze Scholarly PDF Documents

Create Visually Augmented Interactive Readers

Research Prototype Showcase

Here we present several interactive demos to showcase systems you can build with PaperMage and PaperCraft.

Augmenting Research Papers with Author Talk Videos

Demo Paper Presentation

Synergi & Threddy

Clipping Research Threads from Papers for Synthesis and Exploration

Paper Presentation

Paper Plain

Making Medical Research Papers Approachable to Healthcare Consumers

Demo Code Tutorial Paper

LLM Paper Q&A

A GPT-powered PDF QA system with attribution support.

Demo Code Tutorial

Augmenting Citations in Papers with Persistent and Personalized Context

In-Production Paper Presentation

Localizing Incoming Citations from Follow on Papers in the Margins

Automatic highlights for skimming support of scientific papers

In-Production Paper

Augmenting Papers with Just-in-Time Definitions of Terms and Symbols

Founding Project Demo Paper

Publications

Semantic reader project overview.

The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces Kyle Lo, Joseph Chee Chang, Andrew Head, Jonathan Bragg, Amy X. Zhang, Cassidy Trier, Chloe Anastasiades, Tal August, Russell Authur, Danielle Bragg, Erin Bransom, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Yen-Sung Chen, Evie (Yu-Yen) Cheng, Yvonne Chou, Doug Downey, Rob Evans, Raymond Fok, F.Q. Hu, Regan Huff, Dongyeop Kang, Tae Soo Kim, Rodney Michael Kinney, A. Kittur, Hyeonsu B Kang, Egor Klevak, Bailey Kuehl, Michael Langan, Matt Latzke, Jaron Lochner, Kelsey MacMillan, Eric Stuart Marsh, Tyler Murray, Aakanksha Naik, Ngoc-Uyen Nguyen, Srishti Palani, Soya Park, Caroline Paulic, Napol Rachatasumrit, Smita R Rao, P. Sayre, Zejiang Shen, Pao Siangliulue, Luca Soldaini, Huy Tran, Madeleine van Zuylen, Lucy Lu Wang, Christopher Wilhelm, Caroline M Wu, Jiangjiang Yang, Angele Zamarron, Marti A. Hearst, Daniel S. Weld . ArXiv. 2023 .

Interactive and Intelligent Reading Interfaces

Qlarify: Bridging Scholarly Abstracts and Papers with Recursively Expandable Summaries Raymond Fok, Joseph Chee Chang, Tal August, Amy X. Zhang, Daniel S. Weld . ArXiv. 2023 .

Papeos: Augmenting Research Papers with Talk Videos Tae Soo Kim, Matt Latzke, Jonathan Bragg, Amy X. Zhang, Joseph Chee Chang . The ACM Symposium on User Interface Software and Technology. 2023 .

Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking Hyeonsu B Kang, Sherry Wu, Joseph Chee Chang, A. Kittur . The ACM Symposium on User Interface Software and Technology. 2023 .

🏆 Best Paper Award CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context Joseph Chee Chang, Amy X. Zhang, Jonathan Bragg, Andrew Head, Kyle Lo, Doug Downey, Daniel S. Weld . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2023 .

Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections Srishti Palani, Aakanksha Naik, Doug Downey, Amy X. Zhang, Jonathan Bragg, Joseph Chee Chang . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2023 .

CiteRead: Integrating Localized Citation Contexts into Scientific Paper Reading Napol Rachatasumrit, Jonathan Bragg, Amy X. Zhang, Daniel S. Weld . 27th International Conference on Intelligent User Interfaces. 2022 .

🏆 Best Paper Award Math Augmentation: How Authors Enhance the Readability of Formulas using Novel Visual Design Practices Andrew Head, Amber Xie, Marti A. Hearst . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2022 .

Scim: Intelligent Skimming Support for Scientific Papers Raymond Fok, Hita Kambhamettu, Luca Soldaini, Jonathan Bragg, Kyle Lo, Andrew Head, Marti A. Hearst, Daniel S. Weld . Proceedings of the 28th International Conference on Intelligent User Interfaces. 2022 .

Exploring Team-Sourced Hyperlinks to Address Navigation Challenges for Low-Vision Readers of Scientific Papers Soya Park, Jonathan Bragg, Michael Chang, K. Larson, Danielle Bragg . Proceedings of the ACM on Human-Computer Interaction. 2022 .

Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing Tal August, Lucy Lu Wang, Jonathan Bragg, Marti A. Hearst, Andrew Head, Kyle Lo . ACM Transactions on Computer-Human Interaction. 2022 . Presentation at CHI 2024.

Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature Hyeonsu B Kang, Joseph Chee Chang, Yongsung Kim, A. Kittur . Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 2022 .

🏆 Best Paper Award SciA11y: Converting Scientific Papers to Accessible HTML Lucy Lu Wang, Isabel Cachola, Jonathan Bragg, Evie (Yu-Yen) Cheng, Chelsea Hess Haupt, Matt Latzke, Bailey Kuehl, Madeleine van Zuylen, Linda M. Wagner, Daniel S. Weld . Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility. 2021 .

Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols Andrew Head, Kyle Lo, Dongyeop Kang, Raymond Fok, Sam Skjonsberg, Daniel S. Weld, Marti A. Hearst . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2020 .

Open Research Resources: Libraries, Models, Datasets

🏆 Best Paper Award PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents Kyle Lo, Zejiang Shen, Benjamin Newman, Joseph Chee Chang, Russell Authur, Erin Bransom, Stefan Candra, Yoganand Chandrasekhar, Regan Huff, Bailey Kuehl, Amanpreet Singh, Chris Wilhelm, Angele Zamarron, Marti A. Hearst, Daniel S. Weld, Doug Downey, Luca Soldaini. Conference on Empirical Methods in Natural Language Processing: Demos. 2023.

A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents Benjamin Newman, Luca Soldaini, Raymond Fok, Arman Cohan, Kyle Lo . undefined. 2023 .

🏆 Best Paper Award LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization Kalpesh Krishna, Erin Bransom, Bailey Kuehl, Mohit Iyyer, Pradeep Dasigi, Arman Cohan, Kyle Lo . ArXiv. 2023 .

Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents Catherine Chen, Zejiang Shen, D. Klein, G. Stanovsky, Doug Downey, Kyle Lo . ArXiv. 2023 .

The Semantic Scholar Open Data Platform Rodney Michael Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Arman Cohan, Miles Crawford, Doug Downey, Jason Dunkelberger, Oren Etzioni, Rob Evans, Sergey Feldman, Joseph Gorney, D. Graham, F.Q. Hu, Regan Huff, Daniel King, Sebastian Kohlmeier, Bailey Kuehl, Michael Langan, Daniel Lin, Haokun Liu, Kyle Lo, Jaron Lochner, Kelsey MacMillan, Tyler Murray, Christopher Newell, Smita R Rao, Shaurya Rohatgi, P. Sayre, Zejiang Shen, Amanpreet Singh, Luca Soldaini, Shivashankar Subramanian, A. Tanaka, Alex D Wade, Linda M. Wagner, Lucy Lu Wang, Christopher Wilhelm, Caroline Wu, Jiangjiang Yang, Angele Zamarron, Madeleine van Zuylen, Daniel S. Weld . ArXiv. 2023 .

VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups Zejiang Shen, Kyle Lo, Lucy Lu Wang, Bailey Kuehl, Daniel S. Weld, Doug Downey . Transactions of the Association for Computational Linguistics. 2021 .

Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions Dongyeop Kang, Andrew Head, Risham Sidhu, Kyle Lo, Daniel S. Weld, Marti A. Hearst . Proceedings of the First Workshop on Scholarly Document Processing @ ACL. 2020 .

See the  Project Overview Paper  to see a full list of contributors. † For questions and inquiries, please contact Joseph Chee Chang (PaperCraft & Intelligent reading interfaces), or Kyle Lo and Luca Soldaini (PaperMage & Scientific document processing).

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Intelligent reading interfaces research, scientific document processing research, research libraries and tooling.

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10 Powerful AI Tools for Academic Research

• Serra Ardem

AI is no longer science fiction, but a powerful ally in the academic realm. With AI by their side, researchers can free themselves from the burden of tedious tasks, and push the boundaries of knowledge. However, they must use AI carefully and ethically, as these practices introduce new considerations regarding data integrity, bias mitigation, and the preservation of academic rigor.

In this blog, we will:

• Highlight the increasing role of AI in academic research
• List 10 best AI tools for academic research, with a focus on each one’s strengths
• Share 5 best practices on how to use AI tools for academic research

Let’s dig in…

The Role of AI in Academic Research

AI tools for academic research hold immense potential, as they can analyze massive datasets and identify complex patterns. These tools can assist in generating new research questions and hypotheses, navigate mountains of academic literature to find relevant information, and automate tedious tasks like data entry.

Let’s take a look at the benefits AI tools offer for academic research:

• Supercharged literature reviews: AI can sift through vast amounts of academic literature, and pinpoint relevant studies with far greater speed and accuracy than manual searches.
• Accelerated data analysis: AI tools can rapidly analyze large datasets and uncover intricate insights that might otherwise be overlooked, or time-consuming to identify manually.
• Enhanced research quality: Helping with grammar checking, citation formatting, and data visualization, AI tools can lead to a more polished and impactful final product.
• Automation of repetitive tasks: By automating routine tasks, AI can save researchers time and effort, allowing them to focus on more intellectually demanding tasks of their research.
• Predictive modeling and forecasting: AI algorithms can develop predictive models and forecasts, aiding researchers in making informed decisions and projections in various fields.
• Cross-disciplinary collaboration: AI fosters collaboration between researchers from different disciplines by facilitating communication through shared data analysis and interpretation.

Now let’s move on to our list of 10 powerful AI tools for academic research, which you can refer to for a streamlined, refined workflow. From formulating research questions to organizing findings, these tools can offer solutions for every step of your research.

1. HyperWrite

For: hypothesis generation

HyperWrite’s Research Hypothesis Generator is perfect for students and academic researchers who want to formulate clear and concise hypotheses. All you have to do is enter your research topic and objectives into the provided fields, and then the tool will let its AI generate a testable hypothesis. You can review the generated hypothesis, make any necessary edits, and use it to guide your research process.

Pricing: You can have a limited free trial, but need to choose at least the Premium Plan for additional access. See more on pricing here .

2. Semantic Scholar

For: literature review and management

With over 200 million academic papers sourced, Semantic Scholar is one of the best AI tools for literature review. Mainly, it helps researchers to understand a paper at a glance. You can scan papers faster with the TLDRs (Too Long; Didn’t Read), or generate your own questions about the paper for the AI to answer. You can also organize papers in your own library, and get AI-powered paper recommendations for further research.

Pricing: free

For: summarizing papers

Apparently, Elicit is a huge booster as its users save up to 5 hours per week. With a database of 125 million papers, the tool will enable you to get one-sentence, abstract AI summaries, and extract details from a paper into an organized table. You can also find common themes and concepts across many papers. Keep in mind that Elicit works best with empirical domains that involve experiments and concrete results, like biomedicine and machine learning.

Pricing: Free plan offers 5,000 credits one time. See more on pricing here .

For: transcribing interviews

Supporting 125+ languages, Maestra’s interview transcription software will save you from the tedious task of manual transcription so you can dedicate more time to analyzing and interpreting your research data. Just upload your audio or video file to the tool, select the audio language, and click “Submit”. Maestra will convert your interview into text instantly, and with very high accuracy. You can always use the tool’s built-in text editor to make changes, and Maestra Teams to collaborate with fellow researchers on the transcript.

Pricing: With the “Pay As You Go” plan, you can pay for the amount of work done. See more on pricing here .

5. ATLAS.ti

For: qualitative data analysis

Whether you’re working with interview transcripts, focus group discussions, or open-ended surveys, ATLAS.ti provides a set of tools to help you extract meaningful insights from your data. You can analyze texts to uncover hidden patterns embedded in responses, or create a visualization of terms that appear most often in your research. Plus, features like sentiment analysis can identify emotional undercurrents within your data.

Pricing: Offers a variety of licenses for different purposes. See more on pricing here .

6. Power BI

For: quantitative data analysis

Microsoft’s Power BI offers AI Insights to consolidate data from various sources, analyze trends, and create interactive dashboards. One feature is “Natural Language Query”, where you can directly type your question and get quick insights about your data. Two other important features are “Anomaly Detection”, which can detect unexpected patterns, and “Decomposition Tree”, which can be utilized for root cause analysis.

Pricing: Included in a free account for Microsoft Fabric Preview. See more on pricing here .

7. Paperpal

For: writing research papers

As a popular AI writing assistant for academic papers, Paperpal is trained and built on 20+ years of scholarly knowledge. You can generate outlines, titles, abstracts, and keywords to kickstart your writing and structure your research effectively. With its ability to understand academic context, the tool can also come up with subject-specific language suggestions, and trim your paper to meet journal limits.

Pricing: Free plan offers 5 uses of AI features per day. See more on pricing here .

For: proofreading

With Scribbr’s AI Proofreader by your side, you can make your academic writing more clear and easy to read. The tool will first scan your document to catch mistakes. Then it will fix grammatical, spelling and punctuation errors while also suggesting fluency corrections. It is really easy to use (you can apply or reject corrections with 1-click), and works directly in a DOCX file.

Pricing: The free version gives a report of your issues but does not correct them. See more on pricing here .

9. Quillbot

For: detecting AI-generated content

Want to make sure your research paper does not include AI-generated content? Quillbot’s AI Detector can identify certain indicators like repetitive words, awkward phrases, and an unnatural flow. It’ll then show a percentage representing the amount of AI-generated content within your text. The tool has a very user-friendly interface, and you can have an unlimited number of checks.

10. Lateral

For: organizing documents

Lateral will help you keep everything in one place and easily find what you’re looking for. 

With auto-generated tables, you can keep track of all your findings and never lose a reference. Plus, Lateral uses its own machine learning technology (LIP API) to make content suggestions. With its “AI-Powered Concepts” feature, you can name a Concept, and the tool will recommend relevant text across all your papers.

Pricing: Free version offers 500 Page Credits one-time. See more on pricing here .

How to Use AI Tools for Research: 5 Best Practices

Before we conclude our blog, we want to list 5 best practices to adopt when using AI tools for academic research. They will ensure you’re getting the most out of AI technology in your academic pursuits while maintaining ethical standards in your work.

• Always remember that AI is an enhancer, not a replacement. While it can excel at tasks like literature review and data analysis, it cannot replicate the critical thinking and creativity that define strong research. Researchers should leverage AI for repetitive tasks, but dedicate their own expertise to interpret results and draw conclusions.
• Verify results. Don’t take AI for granted. Yes, it can be incredibly efficient, but results still require validation to prevent misleading or inaccurate results. Review them thoroughly to ensure they align with your research goals and existing knowledge in the field.
• Guard yourself against bias. AI tools for academic research are trained on existing data, which can contain social biases. You must critically evaluate the underlying assumptions used by the AI model, and ask if they are valid or relevant to your research question. You can also minimize bias by incorporating data from various sources that represent diverse perspectives and demographics.
• Embrace open science. Sharing your AI workflow and findings can inspire others, leading to innovative applications of AI tools. Open science also promotes responsible AI development in research, as it fosters transparency and collaboration among scholars.
• Stay informed about the developments in the field. AI tools for academic research are constantly evolving, and your work can benefit from the recent advancements. You can follow numerous blogs and newsletters in the area ( The Rundown AI is a great one) , join online communities, or participate in workshops and training programs. Moreover, you can connect with AI researchers whose work aligns with your research interests.

Frequently Asked Questions

Is chatgpt good for academic research.

ChatGPT can be a valuable tool for supporting your academic research, but it has limitations. You can use it for brainstorming and idea generation, identifying relevant resources, or drafting text. However, ChatGPT can’t guarantee the information it provides is entirely accurate or unbiased. In short, you can use it as a starting point, but never rely solely on its output.

Can I use AI for my thesis?

Yes, but it shouldn’t replace your own work. It can help you identify research gaps, formulate a strong thesis statement, and synthesize existing knowledge to support your argument. You can always reach out to your advisor and discuss how you plan to use AI tools for academic research .

Can AI write review articles?

AI can analyze vast amounts of information and summarize research papers much faster than humans, which can be a big time-saver in the literature review stage. Yet it can struggle with critical thinking and adding its own analysis to the review. Plus, AI-generated text can lack the originality and unique voice that a human writer brings to a review.

Can professors detect AI writing?

Yes, they can detect AI writing in several ways. Software programs like Turnitin’s AI Writing Detection can analyze text for signs of AI generation. Furthermore, experienced professors who have read many student papers can often develop a gut feeling about whether a paper was written by a human or machine. However, highly sophisticated AI may be harder to detect than more basic versions.

Can I do a PhD in artificial intelligence?

Yes, you can pursue a PhD in artificial intelligence or a related field such as computer science, machine learning, or data science. Many universities worldwide offer programs where you can delve deep into specific areas like natural language processing, computer vision, and AI ethics. Overall, pursuing a PhD in AI can lead to exciting opportunities in academia, industry research labs, and tech companies.

This blog shared 10 powerful AI tools for academic research, and highlighted each tool’s specific function and strengths. It also explained the increasing role of AI in academia, and listed 5 best practices on how to adopt AI research tools ethically.

AI tools hold potential for even greater integration and impact on research. They are likely to become more interconnected, which can lead to groundbreaking discoveries at the intersection of seemingly disparate fields. Yet, as AI becomes more powerful, ethical concerns like bias and fairness will need to be addressed. In short, AI tools for academic research should be utilized carefully, with a keen awareness of their capabilities and limitations.

About Serra Ardem

Serra Ardem is a freelance writer and editor based in Istanbul. For the last 8 years, she has been collaborating with brands and businesses to tell their unique story and develop their verbal identity.

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• NATURE INDEX
• 18 September 2024

Can AI be used to assess research quality?

• Jackson Ryan 0

Jackson Ryan is a freelance science journalist in Sydney, Australia.

You can also search for this author in PubMed   Google Scholar

Illustration: Neil Webb

Do squirrel surgeons generate more citation impact? The question seems ludicrous, or perhaps the start of a bad joke. But the question, posed by data scientist, Mike Thelwall, was not a joke. It was a test. Thelwall, who works at the University of Sheffield, UK, had been assessing the ability of large language models (LLMs) to evaluate academic papers against the criteria of the research excellence framework (REF), the United Kingdom’s national audit of research quality. After giving a custom version of ChatGPT the REF’s criteria, he fed 51 of his own research works into the model and was surprised by the chatbot’s capability to produce plausible reports. “There’s nothing in the reports themselves to say that it’s not written by a human expert,” he says. “That’s an astonishing achievement.”

However, the squirrel paper really threw the model. Thelwall had created the paper by taking one of his own rejected manuscripts on whether male surgeons generate more citation impacts than female surgeons, and to make it nonsensical he replaced ‘male’ with ‘squirrel’, ‘female’ with ‘human’ and any references to gender he switched to ‘species’ throughout the paper. His ChatGPT model could not determine that ‘squirrel surgeons’ were not a real thing during evaluation and the chatbot scored the paper highly.

Nature Index 2024 Artificial intelligence

Thelwall also found that the model was not particularly successful at applying a score based on REF guidelines to the 51 papers that were assessed. He concluded that as much as the model could produce authentic-sounding reports, it wasn’t capable of evaluating quality.

The rapid rise of generative artificial intelligence (AI) such as ChatGPT and image generators such as DALL-E has led to increasing discussion about where AI might fit into research evaluation. Thelwall’s study 1 , published in May, is just one piece of a puzzle that academics, research institutions and funders are trying to piece together. It comes as researchers also grapple with the many other ways that AI is affecting science and the developing guidelines that are springing up around its use. These discussions, however, have rarely focused on providing a steer on how AI might be used in assessing research quality. “That is the next frontier,” says Gitanjali Yadav, a structural biologist at India’s National Institute of Plant Genome Research in New Delhi, and member of the AI working group at the Coalition for Advancing Research Assessment, a global initiative to improve research assessment practice.

Notably, the AI boom also coincides with growing calls to rethink how research outputs are evaluated. Over the past decade, there have been calls to move away from publication-based metrics such as journal impact factors and citation counts, which have shown to be prone to manipulation and bias . Integrating AI into this process at such a time provides an opportunity to incorporate it in new mechanisms for understanding, and measuring, the quality and impact of research. But it also raises important questions about whether AI can fully aid research evaluation, or whether it has the potential to exacerbate issues and even create further problems.

Quality assessments

Research quality is difficult to define, although there is a general consensus that good quality research is underpinned by honesty, rigour, originality and impact. There’s a wide variety of mechanisms, each operating at different levels of the research ecosystem, to assess these traits, and myriad ways to do so. The bulk of research-quality assessment happens in the peer-review process, which is, in many cases, the first external quality review performed on a new piece of science. Many journals have been using a suite of AI tools to supplement this process for some time. There’s AI to match manuscripts with suitable reviewers, algorithms that detect plagiarism and check for statistical flaws, and other tools aimed at strengthening integrity by catching data manipulation.

More recently, the rise of generative AI has seen a rush of research aimed at exploring how well an LLM might be able to aid peer review — and whether scientists would trust those tools to do so. Some publishers allow AI to assist in manuscript preparation, if adequately disclosed, but do not allow its use in peer review. Even so, there’s a growing belief among academics in the ability of these tools, particularly those based on natural language processing and LLMs.

Source: Ref. 2

A study published in July this year 2 , led by computer science PhD student, Weixin Liang, in the lab of biomedical data scientist, James Zou, at Stanford University in California, assessed the capability of one LLM, GPT-4, to provide feedback on manuscripts. The study asked researchers to upload a manuscript and have it assessed by their AI model. Researchers then completed a survey evaluating the feedback and how it compared with human reviewers. It received 308 responses, with more than half describing the AI-generated reviews as “helpful” or “very helpful”. But the study did highlight some problems with that feedback: it was sometimes generic and struggled to provide in-depth critiques.

Zou thinks this doesn’t necessarily preclude the use of such tools in certain situations. One particular example he mentions is early-career researchers working on the first draft of a paper. They could upload a draft to a bespoke LLM and receive commentary about deficiencies or errors in their draft. But given the laborious and somewhat repetitive nature of peer review, some academics worry that there could be a tendency to lean on the outputs from a generative AI system capable of delivering reports. “There’s no kind of glory or funding associated with peer review. It’s just seen as a scientific duty,” says Elizabeth Gadd, head of research culture and assessment at Loughborough University, UK. There is already evidence that peer reviewers are using ChatGPT and other chatbots to some extent , despite the rules put in place by some journal publishers.

Thelwall believes there’s more that AI could do in helping peer reviewers to evaluate research quality, but there is reason to move slowly. “We just need lots of testing,” he says. “And not just technical testing, but also pragmatic testing, where we gain confidence that if we provide the AI to the reviewers, for example, that they won’t abuse it.”

Yadav sees great benefit in AI as a time-saving tool and has been working with it to help rapidly assess wildlife imagery from field-based cameras in India, but she sees peer review as too important to the scientific community to hand over to the bots. “I’m personally absolutely against peer review being done by AI,” she says.

Quality savings

One of the most discussed benefits of using AI is the idea that it could free up time. This is particularly apparent in institutional and national systems of evaluating research — some of which have incorporated AI. For instance, one funder in Australia, the National Health and Medical Research Council (NHMRC), already uses AI through “a hybrid model combining machine learning and mathematical optimisation techniques” to identify suitable human peer reviewers to judge grant proposals. The system helps to remove one of the administrative bottlenecks in the evaluation process, but it’s where the AI use ends. An NHMRC spokesperson says the agency “does not use artificial intelligence, in any form, to directly assist with research quality evaluation” itself.

Even using AI for such administrative support could be a major resource saving, however, especially for large national assessments such as the REF. Thelwall says the exercise is known for its incredible drain on researchers’ time. More than 1,000 academics help to assess research quality in the REF and it takes them about half a year to get it done.

“If we can automate evaluations”, says Thelwall, then “it would be a massive productivity boost”. And there’s potential for huge savings: the most recent REF, in 2021, was estimated to have cost around £471 million (US$618 million).

Similarly, New Zealand’s assessment of researchers, the Performance Based Research Fund, has previously been described by Tim Fowler, chief executive of the government’s Tertiary Education Commission, as a “backbreaking” exercise. In it, academics submit portfolios for assessment, placing an extreme burden on them and institutions. In April, the government scrapped it and a working group has been charged with delivering a new plan by February 2025.

These examples suggest AI’s major potential to create more efficiency, at least for large, bureaucratic, assessment systems and processes. At the same time, the technology is developing as perspectives on what constitutes research quality are evolving and becoming more nuanced. “How you might have defined research quality in the early twentieth century is not how you define it now,” says Marnie Hughes-Warrington, deputy vice-chancellor of research and enterprise at the University of South Australia in Adelaide. Hughes-Warrington is a member of the Excellence in Research Australia transition group, which is considering the future of the country’s assessment exercise after a review in 2021 found that it placed a significant burden on universities. She says the research community is increasingly recognizing the need to assess more “non-traditional research outputs” — such as policy documents, creative works, exhibitions — and then beyond to social and economic impacts.

As the conversations are happening alongside the AI boom, it makes sense that new tools could fit into revised methods of research-quality evaluation. For instance, Hughes-Warrington points to how AI is already being used to detect image manipulation in journals or to synthesize data from systems used to uniquely identify researchers and documents. Applying these kinds of methods would be consistent with the mission of institutions such as universities and national bodies. “Why wouldn’t organizations, driven by curiosity and research, implement new ways of doing things?” she says.

However, Hughes-Warrington also highlights where incorporating AI will meet resistance. There’s privacy, copyright and data-security concerns to acknowledge, inherent biases in the tools to overcome and a need to consider the context in which research assessments take place, such as how impacts will differ across disciplines, institutions and countries.

Gadd isn’t against incorporating AI and says she is noticing it appear more often in discussions around research quality. But she warns that researchers are already one of the most assessed professions in the world. “My own general view on this is that we assess too much,” she said. “Are we looking at using AI to solve a problem that’s of our own making?”

Having seen how bibliometrics-based assessments can damage the sector , with metrics such as journal impact factors misused as a substitute for quality and shown to hinder early-career researchers and diversity, Gadd is concerned about how AI might be implemented, especially if models are trained on these same metrics. She also says decisions involving allocation of promotions, funding or other rewards will always need human involvement to a far greater extent. “You have to be very cautious”, she says, about shifting to technology “to make decisions which are going to affect lives”.

Gadd has worked extensively in developing SCOPE, a framework for responsible research evaluation by the International Network of Research Management Societies, a global organization that brings research management societies together to coordinate activities and share knowledge in the field. She says one of the key principles of the scheme is to “evaluate only where necessary” and, in that perhaps, there is a lesson for how we should think about incorporating AI. “If we evaluated less, we could do it to a higher standard,” she says. “Maybe” AI can support that process, but a “lot of the arguments and worries we’re having about AI, we had about bibliometrics.”

Nature 633 , S18-S20 (2024)

doi: https://doi.org/10.1038/d41586-024-02989-z

This article is part of Nature Index 2024 Artificial intelligence , an editorially independent supplement. Advertisers have no influence over the content. For more information about Nature Index, see the homepage .

Thelwall, M. J . Data Inform. Sci. 9 , 1–21 (2024).

Article   Google Scholar  

Liang, W. et al. NEJM AI https://doi.org/10.1056/AIoa2400196 (2024).

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Researching in the Literature with AI Tools

These tools help us explore and analyze scholarly literature and identify citations through graphic visualizations. You can expand your search strategy beyond keywords to investigate the connections between scholarly articles based on a variety of additional factors. You can also use these tools to help you develop your skills in reading research and scholarly articles.

Table of AI Literature Research Tools

CSU Libraries does not provide institutional access to any of these products at this time.

This table was last updated on 30 August, 2024.

Be Smart About AI Tools

Your critical thinking skills are what power these tools.

If the tool:

• Locates new papers based on either a question or on initial papers you suggest , look for help documentation that explains what databases or collections of research are being searched. No tool actually searches all information or even all scholarly sources! What might be missing? Recognize that these tools are not designed to return comprehensive results and are best for supplementing and enhancing database searches.
•   Recommends papers based on 'similarity' to other papers , check the tool for any help documentation that explains how 'similarity' is calculated. Try multiple tools with this feature using the same paper(s) to see if different 'similar' results are suggested.
• Locates and/or summarizes relevant papers based on a question you ask , try asking the same question using different structures and wording to compare results. Check how relevant the results actually are for answering the question you asked. Check the references of papers that seem most relevant to find additional sources.
• Extracts data or sections of paper contents or makes summaries from data/paper contents , then the tool should link back to exactly where in a specific research article that information was extracted and synthesized from. You should ALWAYS review these to ensure accuracy of the summary!
• Allows you to "chat" with a paper by asking it questions and using AI to pull answers from the paper's contents, then the tool should link back to exactly where in the paper that information was extracted and synthesized from. You should ALWAYS review these to ensure accuracy of the answers!
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Title: can llms generate novel research ideas a large-scale human study with 100+ nlp researchers.

Abstract: Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome.

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5 AI Tools for Interacting with Research Papers

In this comprehensive blog post, I will delve into 5 AI tools designed and developed to facilitate interactions with research papers.

Artificial intelligence (AI) is rapidly transforming the way we research and learn. In the field of academic research, AI is being used to develop tools that help researchers find, understand, and cite research papers more effectively.

However, reading and interacting with research papers can be a daunting task, especially for those who are new to the field. Fortunately, there are many AI-backed tools available that make conversing with research papers more accessible and efficient than ever before.

Here begins the first part (Part-I) of  three-part blog series about the AI tools for interacting with research papers.

If you want to make your academic paper more interactive, you have come to the right place. The purpose of this blog post is to provide insights into a collection of 5 AI-powered tools for interacting with research papers.

Here are 5 AI tools for conversing with academic research papers.

No. #1 ChatPDF

ChatPDF is an AI tool that allows you to extract relevant information from your paper. The user-friendly web application lets you convert your research paper into an interactive chatbot in just a few minutes.

Using ChatPDF is as simple as uploading your research paper and customizing your chatbot.You do not need any coding experience or technical skills to do the same.

This tool is available to anyone, regardless of their technical skills. The AI-backed tool can help academic researchers to extract relevant information from PDF  academic papers.

This handy tool transforms the text within a paper into a format that is simple to search and analyze. This is a useful tool for researchers who need to quickly find specific information in a research paper.

In an earlier blog , I demonstrated elaborately how to use the ChatPDF to turn your academic paper into an interactive chatbot.

No. #2  Humata AI

Hum ata AI is an AI-powered tool that helps you research, learn, and create faster. It can summarize research papers, answer your questions about your paper, and generate new writing based on your documents.

You can access the AI-driven tool by entering the URL ( https://www.humata.ai ) into your browser’s address bar.

Upon accessing the website, the following page will be displayed:

After setting up your account, you can proceed to upload the research paper (PDF format) you want to analyze. Now, you locate the paper on your local machine and easily drag and drop it into the designated section on the Hum ata platform.

Once you have uploaded the paper, the sophisticated AI algorithm s will start analyzing and comprehending the content of your paper. It will take few seconds to process the paper.

Hum ata is based on OpenAI’s ChatGPT , and it can be used to interact with a variety of files, including PDFs, word documents, and spreadsheets.

Once the processing is finished, you can interact with its chatbot. It is located in the left pane of the interface.

The AI chatbot will prompt ly furnish you with clear and comprehensible answers in real-time.

Hum ata AI helps you to answer hard questions related to your research papers. It also summarizes long papers and extracting key points.

You can visit their website  here  to learn more about Hum ata AI and its features.

Overall, Hum ata AI is a powerful tool that leverages AI technology to assist users in various aspects of academic research, data analysis , and document management. It offers features that enhance efficiency, provide valuable insights, and simplify complex information.

No. #3 Perplexity AI

Have you ever wished you could chat with any scholarly content and ask questions about it in natural language? Well, now you can with Perplexity AI, a new AI chat tool that acts as an extremely powerful search engine.

Perplexity AI is an AI-powered tool that  lets you answer your questions in a comprehensive and informative way. It uses large language models and search engines to achieve this, allowing it to provide answers to a wide range of questions.

It is capable of understanding natural language inputs, as well as providing answers to more specific questions.

Perplexity AI is a web crawler that uses machine learning to generate general answers to your queries and then offer a series of website links. The links are to websites that the AI thinks are relevant to your query.

Perplexity AI offers a seamless experience by allowing you to ask any question using simple, everyday language. The beauty of this tool lies in its ability to provide you with comprehensive and informative answers.

It shows you the sources it used to answer your questions and encourages follow-up inquiries.

Sharing your questions and answers with others,  the tool promotes collaborative learning

You can use either of these two methods to access the AI-driven tool:

• Access the Perplexity website

2. Setup the Perplexity Chrome Extension

Perplexity AI is free to use and available on the web and as an app for iPhone users. To use Perplexity AI, you need to visit their website  here  or download their app  here .

You can enter your question in the box and the AI-assisted tool will provide you with an answer based on the ChatGPT .

In a previous blog , I demonstrated elaborately on how to use the perplexity AI search engine tool for academic research.

The Perplexity AI is a powerful tool that can help you find relevant information on any topic quickly and accurately.

Besides, you can use the Google Chrome extension to ask contextual questions about the website you are visiting.

No. #4  ChatDOC

Have you ever wished you could chat with any document and ask questions about it in natural language? Well, now you can with ChatDOC, a new AI tool that acts as an AI-powered file-reading assistant.

ChatDOc allows you chat with any paper and get instant answers with cited sources. It is a handy file-reading assistant powered by ChatGPT . 

It is great at quickly pulling out, finding, and summarizing information from various document formats—like .pdf, .docx, .md, and even scanned files.

ChatDOC is free to use and available on the web and as an app for Android users.

You can upload or paste any source of information into the tool and start asking questions. ChatDOC will provide you with responses based on the source and the ChatGPT model parameters.

Users can ask questions and get instant answers from ChatDOc, which can save time and effort. ChatDOc provides cited sources for its answers, which can help users verify the accuracy of the information.

With its versatile abilities, ChatDOC becomes an invaluable tool for efficiently analyzing documents and capturing essential insights.

ChatDOc can be a useful tool for anyone who needs to quickly find information in a document. It can be especially helpful for researchers who need to read and analyze large amounts of text. 

ChatDOC is a powerful tool that can save time and effort for individuals who frequently read and analyze documents. It provides a convenient way to extract information, locate specific details, and summarize content, ultimately enhancing the reading and learning experience

No. #5 PDF.ai

PDF documents are a ubiquitous format for storing and sharing information. However, they can be difficult to interact with, especially if they are large or complex.

PDF.ai is an AI-powered tool that can help you interact with PDF documents in a variety of ways.

With PDF.ai, you can understand the content of a research paper and answer your questions about it in plain English.

This can be helpful if you need to quickly get the main points of your paper.

The state-of-the-art tool can extract tables and data from your article for analysis. Besides, the tool can summarize the data in a table or spreadsheet.

The online application translates the research paper into different languages. This can be beneficial if you read a PDF document in a language you are unfamiliar with and need to read it.

The tool allows you to convert PDF paper to other formats, such as Word, Excel, and PowerPoint. It is capable of handling OCR-scanned research papers   that was scanned from a hard copy.

Upon visiting their website, the first step is to complete the signup process. Following this, a confirmation email will be sent to your provided email address.

Once you confirm your registration through the email link, you can access the tool’s features.

Here is a snapshot depicting the interface for your paper uploading:

Upon uploading your paper, you can begin interacting with the paper. In my case, I used a paper on Bitcoin sourced from their website.

The window below will be visible to you:

It is a powerful tool helps you save time and improve your productivity when working with your research documents.

If you need an affordable and powerful AI-powered tool for interacting with your academic paper, consider using PDF.ai.

You can add the PDF.ai extension to your Google Chrome browser to start interacting effortlessly with any academic papers.

AI tools are essential for enhancing your research skills and knowledge. These tools help you interact with research papers using natural language queries. The AI-powered tools can provide you with summaries, answers, and insights as you read the scholarly content.

You can also use these tools for various tasks such as literature review , citation analysis, text generation, and text synthesis. They have different features, accuracy rates, and prices, but they all provide reliable and useful services for interacting with research papers.

I hope you enjoyed this blog post and found it helpful for your research needs. If you liked this post, please share it with your friends and colleagues who might also benefit from it. Stay tuned for more posts on AI tools and research in the future.

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Eliza Strickland is a Senior Editor at IEEE Spectrum covering AI and biomedical engineering.

When an international team of researchers set out to create an “AI scientist” to handle the whole scientific process, they didn’t know how far they’d get. Would the system they created really be capable of generating interesting hypotheses, running experiments, evaluating the results, and writing up papers?

What they ended up with, says researcher Cong Lu , was an AI tool that they judged equivalent to an early Ph.D. student. It had “some surprisingly creative ideas,” he says, but those good ideas were vastly outnumbered by bad ones. It struggled to write up its results coherently, and sometimes misunderstood its results: “It’s not that far from a Ph.D. student taking a wild guess at why something worked,” Lu says. And, perhaps like an early Ph.D. student who doesn’t yet understand ethics, it sometimes made things up in its papers, despite the researchers’ best efforts to keep it honest.

Lu, a postdoctoral research fellow at the University of British Columbia , collaborated on the project with several other academics, as well as with researchers from the buzzy Tokyo-based startup Sakana AI . The team recently posted a preprint about the work on the ArXiv server. And while the preprint includes a discussion of limitations and ethical considerations, it also contains some rather grandiose language, billing the AI scientist as “the beginning of a new era in scientific discovery,” and “the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models (LLMs) to perform research independently and communicate their findings.”

The AI scientist seems to capture the zeitgeist. It’s riding the wave of enthusiasm for AI for science, but some critics think that wave will toss nothing of value onto the beach.

The “AI for Science” Craze

This research is part of a broader trend of AI for science. Google DeepMind arguably started the craze back in 2020 when it unveiled AlphaFold , an AI system that amazed biologists by predicting the 3D structures of proteins with unprecedented accuracy. Since generative AI came on the scene, many more big   corporate   players have gotten involved. Tarek Besold , a SonyAI senior research scientist who leads the company’s AI for scientific discovery program, says that AI for science is “ a goal behind which the AI community can rally in an effort to advance the underlying technology but—even more importantly—also to help humanity in addressing some of the most pressing issues of our times.”

Yet the movement has its critics. Shortly after a 2023 Google DeepMind paper came out claiming the discovery of 2.2 million new crystal structures (“equivalent to nearly 800 years’ worth of knowledge”), two materials scientists analyzed a random sampling of the proposed structures and said that they found “scant evidence for compounds that fulfill the trifecta of novelty, credibility, and utility.” In other words, AI can generate a lot of results quickly, but those results may not actually be useful.

How the AI Scientist Works

In the case of the AI scientist, Lu and his collaborators tested their system only on computer science, asking it to investigate topics relating to large language models, which power chatbots like ChatGPT and also the AI scientist itself, and the diffusion models that power image generators like DALL-E .

The AI scientist’s first step is hypothesis generation. Given the code for the model it’s investigating, it freely generates ideas for experiments it could run to improve the model’s performance, and scores each idea on interestingness, novelty, and feasibility. It can iterate at this step, generating variations on the ideas with the highest scores. Then it runs a check in Semantic Scholar to see if its proposals are too similar to existing work. It next uses a coding assistant called Aider to run its code and take notes on the results in the format of an experiment journal. It can use those results to generate ideas for follow-up experiments.

The next step is for the AI scientist to write up its results in a paper using a template based on conference guidelines. But, says Lu, the system has difficulty writing a coherent nine-page paper that explains its results—”the writing stage may be just as hard to get right as the experiment stage,” he says. So the researchers broke the process down into many steps: The AI scientist wrote one section at a time, and checked each section against the others to weed out both duplicated and contradictory information. It also goes through Semantic Scholar again to find citations and build a bibliography.

But then there’s the problem of hallucinations —the technical term for an AI making stuff up. Lu says that although they instructed the AI scientist to only use numbers from its experimental journal, “sometimes it still will disobey.” Lu says the model disobeyed less than 10 percent of the time, but “we think 10 percent is probably unacceptable.” He says they’re investigating a solution, such as instructing the system to link each number in its paper to the place it appeared in the experimental log. But the system also made less obvious errors of reasoning and comprehension, which seem harder to fix.

And in a twist that you may not have seen coming, the AI scientist even contains a peer review module to evaluate the papers it has produced. “We always knew that we wanted some kind of automated [evaluation] just so we wouldn’t have to pour over all the manuscripts for hours,” Lu says. And while he notes that “there was always the concern that we’re grading our own homework,” he says they modeled their evaluator after the reviewer guidelines for the leading AI conference NeurIPS and found it to be harsher overall than human evaluators. Theoretically, the peer review function could be used to guide the next round of experiments.

Critiques of the AI Scientist

While the researchers confined their AI scientist to machine learning experiments, Lu says the team has had a few interesting conversations with scientists in other fields. In theory, he says, the AI scientist could help in any field where experiments can be run in simulation. “Some biologists have said there’s a lot of things that they can do in silico,” he says, also mentioning quantum computing and materials science as possible fields of endeavor.

Some critics of the AI for science movement might take issue with that broad optimism. Earlier this year, Jennifer Listgarten , a professor of computational biology at UC Berkeley, published a paper in Nature Biotechnology arguing that AI is not about to produce breakthroughs in multiple scientific domains. Unlike the AI fields of natural language processing and computer vision, she wrote, most scientific fields don’t have the vast quantities of publicly available data required to train models.

Two other researchers who study the practice of science, anthropologist Lisa Messeri of Yale University and psychologist M.J. Crockett of Princeton University, published a 2024 paper in Nature that sought to puncture the hype surrounding AI for science. When asked for a comment about this AI scientist, the two reiterated their concerns over treating “AI products as autonomous researchers.” They argue that doing so risks narrowing the scope of research to questions that are suited for AI, and losing out on the diversity of perspectives that fuels real innovation. “While the productivity promised by ‘the AI Scientist’ may sound appealing to some,” they tell IEEE Spectrum , “producing papers and producing knowledge are not the same, and forgetting this distinction risks that we produce more while understanding less.”

But others see the AI scientist as a step in the right direction. SonyAI’s Besold says he believes it’s a great example of how today’s AI can support scientific research when applied to the right domain and tasks. “This may become one of a handful of early prototypes that can help people conceptualize what is possible when AI is applied to the world of scientific discovery,” he says.

What’s Next for the AI Scientist

Lu says that the team plans to keep developing the AI scientist, and he says there’s plenty of low-hanging fruit as they seek to improve its performance. As for whether such AI tools will end up playing an important role in the scientific process, “I think time will tell what these models are good for,” Lu says. It might be, he says, that such tools are useful for the early scoping stages of a research project, when an investigator is trying to get a sense of the many possible research directions—although critics add that we’ll have to wait for future studies to see if these tools are really comprehensive and unbiased enough to be helpful.

Or, Lu says, if the models can be improved to the point that they match the performance of “a solid third-year Ph.D. student,” they could be a force multiplier for anyone trying to pursue an idea (at least, as long as the idea is in an AI-suitable domain). “At that point, anyone can be a professor and carry out a research agenda,” says Lu. “That’s the exciting prospect that I’m looking forward to.”

• AI Copilots Are Changing How Coding Is Taught ›
• Supercomputer Emulator: AI’s New Role in Science ›
• AI for Science ›

Eliza Strickland is a senior editor at IEEE Spectrum , where she covers AI, biomedical engineering, and other topics. She holds a master’s degree in journalism from Columbia University.

AI can generate Scientific Papers using ChatGpt/LLM and data Analytics by IBM Watson. But Research papers/ Thesis generation does not always means Innovation

or discoveries by the authors.

In India there is very popular slogan: "Talk less,Work More" The Slogan is used to increase

Labour productivity by motivating the Workers. Our Management & Media use voluminous amounts of words

to emotionally exploit Workers and common people.

Human emotions can be useful for better purposes by AI. AI research will be fruitful for Material Science and Biotechnology/ Pharmaceuticals industry for finding complex molecules, Solving

Power systems complex Matrix operations

This sounds interesting but I'm afraid that without specific results it's all hand-waving. Doug Lenat took a shot at this with "AM" back in the 1970s. Acceptance was mixed. We can certainly take the same approach today with 10^6 more computing power, maybe 10^12 if there was reason to do so. But perhaps that's not even the problem.

So AI is lying. In spite of being instructed not to. Fast, powerful, deceptive, and not controllable. I think that is exceptionally scary.

India Backs Small Nuclear Reactors to Power Heavy Industry

Predicting malicious behavior on x before it happens, barrier breaker shapes aerospace engineering's future, related stories, how and why gary marcus became ai's leading critic, how "long context" improves chatbots' attention spans, amazon's secret weapon in chip design is amazon.

Building Contextually Faithful RAG Applications with SFR-RAG

Retrieval Augmented Generation (RAG) has not only gained steam as one of the most invested areas of research in generative AI but also gathered considerable popularity and commercialization opportunities. RAG is typically applied to question-answering problems, where certain external contextual information retrieved from a data source (potentially private) is provided as part of the question and the generated answer is expected to be factually grounded on the contextual clues. RAG features a retriever , which retrieves relevant knowledge, and a large language model (LLM) that generates an answer faithfully or recognizes if the contextual content is irrelevant or contradicting.

At Salesforce AI Research, we understand the importance of faithfulness and accuracy when building RAG systems that rely heavily on the performance of the LLM. Thus, we introduce SFR-RAG, a 9-billion-parameter language model trained with a significant emphasis on reliable, precise, and faithful contextual generation abilities specific to real-world RAG use cases and relevant agentic tasks. They include precise factual knowledge extraction, distinguishing relevant against distracting contexts, citing appropriate sources along with answers, producing complex and multi-hop reasoning over multiple contexts, consistent format following, as well as refraining from hallucination over unanswerable queries.

To reliably evaluate LLMs in contextual question-answering tasks that are relevant to RAG, we also release ContextualBench , an evaluation suite consisting of 7 contextual benchmarks, such as HotpotQA and 2WikiHopQA , that are measured with consistent setups. 

SFR-RAG surpasses GPT-4o and achieves the state of the art in 3 out of 7 benchmarks in ContextualBench, and overwhelmingly outperforms Command-R+ with 10 times fewer parameters. SFR-RAG is also shown to largely overshadow notable baselines in respecting the context information strongly and faithfully, even when the contextual facts are fabricated, altered, removed or contradicting.

Reliable RAG Application with A New Chat Template

Most language models come with a standard chat template with 3 conversational roles: System, User, and Assistant. However, as LLMs take on more complex use cases like RAG, where the models have to perform multiple steps of reasoning and tool uses before arriving at the final answer. Common implementations usually place these non-conversational steps inside the Assistant turn. There are several disadvantages to this design:

• Security and privacy issues may arise if such internal data processing steps involve sensitive information because the steps may be shown to the users. 
• Application reliability is uncertain as those reasoning steps and tool use outputs need to be parsed using keywords produced in the Assistant turn, which the model may fail to generate. 
• Training LLMs for complex RAG tasks is not straightforward because we need to perform customized token masking on parts of the Assistant turn. It is also difficult to fine-tune the LLMs for safety when malicious prompts and instructions may be injected as part of the contextual content.

To solve those issues, we propose a simple modification of the chat template by introducing 2 optional roles: Thought and Observation.

• Thought is where the LLM may freely talk to itself, perform actions, or reason. 
• Observation is where external contextual information is housed. 

The separation of intermediate thoughts and function returned results from the Assistant turn allows us to easily fine-tune the LLM without tedious masking logic or a keyword parser. It also helps developers build RAG applications with ease as they can display or hide the thoughts and retrieved documents from the user according to their use cases, and extract contents without a cumbersome and unreliable parser. More importantly, the Assistant turn is now relieved from the extra responsibilities and may now focus on delivering user-friendly responses.

SFR-RAG Contextual Performances

SFR-RAG achieves the state of the art in 3 out of 7 benchmarks in the ContextualBench suite, with the highest average score. SFR-RAG has the highest margin in 2WikiHopQA. It outperforms Command-R+ in almost all benchmarks, with 10 times fewer parameters.

SFR-RAG is robust and resilient to novel changes to the context documents as evaluated by the FaithEval suite, which measures how faithful to the context a language model is. As shown in Figure 3, SFR-RAG achieves higher scores in all categories, namely Counterfactual, Unknown, and Conflict. This means the model is faithful to the context even if the facts are changed or become counter-intuitive (Counterfactual). The model can also recognize if the context does not contain the answer (Unknown) and it contains conflicting information (Conflict). The results indicate that SFR-RAG is less prone to hallucination than alternatives, which is the utmost important criterion for building a reliable RAG application.

SFR-RAG will be made available via API soon. Any unreleased services or features referenced here are not currently available and may not be delivered on time or at all. Customers should make their purchase decisions based upon features that are currently available.

Learn More:

Paper: https://arxiv.org/pdf/2409.09916

ContextualBench: Coming soon!

Suggestions or feedback?

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Enhancing LLM collaboration for smarter, more efficient solutions

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Ever been asked a question you only knew part of the answer to? To give a more informed response, your best move would be to phone a friend with more knowledge on the subject.

This collaborative process can also help large language models (LLMs) improve their accuracy. Still, it’s been difficult to teach LLMs to recognize when they should collaborate with another model on an answer. Instead of using complex formulas or large amounts of labeled data to spell out where models should work together, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have envisioned a more organic approach.

Their new algorithm, called “Co-LLM,” can pair a general-purpose base LLM with a more specialized model and help them work together. As the former crafts an answer, Co-LLM reviews each word (or token) within its response to see where it can call upon a more accurate answer from the expert model. This process leads to more accurate replies to things like medical prompts and math and reasoning problems. Since the expert model is not needed at each iteration, this also leads to more efficient response generation. To decide when a base model needs help from an expert model, the framework uses machine learning to train a “switch variable,” or a tool that can indicate the competence of each word within the two LLMs’ responses. The switch is like a project manager, finding areas where it should call in a specialist. If you asked Co-LLM to name some examples of extinct bear species, for instance, two models would draft answers together. The general-purpose LLM begins to put together a reply, with the switch variable intervening at the parts where it can slot in a better token from the expert model, such as adding the year when the bear species became extinct.

“With Co-LLM, we’re essentially training a general-purpose LLM to ‘phone’ an expert model when needed,” says Shannon Shen, an MIT PhD student in electrical engineering and computer science and CSAIL affiliate who’s a lead author on a new paper about the approach . “We use domain-specific data to teach the base model about its counterpart’s expertise in areas like biomedical tasks and math and reasoning questions. This process automatically finds the parts of the data that are hard for the base model to generate, and then it instructs the base model to switch to the expert LLM, which was pretrained on data from a similar field. The general-purpose model provides the ‘scaffolding’ generation, and when it calls on the specialized LLM, it prompts the expert to generate the desired tokens. Our findings indicate that the LLMs learn patterns of collaboration organically, resembling how humans recognize when to call upon an expert to fill in the blanks.”

A combination of flexibility and factuality

Imagine asking a general-purpose LLM to name the ingredients of a specific prescription drug. It may reply incorrectly, necessitating the expertise of a specialized model. To showcase Co-LLM’s flexibility, the researchers used data like the  BioASQ medical set to couple a base LLM with expert LLMs in different domains, like the  Meditron model , which is pretrained on unlabeled medical data. This enabled the algorithm to help answer inquiries a biomedical expert would typically receive, such as naming the mechanisms causing a particular disease. For example, if you asked a simple LLM alone to name the ingredients of a specific prescription drug, it may reply incorrectly. With the added expertise of a model that specializes in biomedical data, you’d get a more accurate answer. Co-LLM also alerts users where to double-check answers. Another example of Co-LLM’s performance boost: When tasked with solving a math problem like “a3 · a2 if a=5,” the general-purpose model incorrectly calculated the answer to be 125. As Co-LLM trained the model to collaborate more with a large math LLM called  Llemma , together they determined that the correct solution was 3,125.

Co-LLM gave more accurate replies than fine-tuned simple LLMs and untuned specialized models working independently. Co-LLM can guide two models that were trained differently to work together, whereas other effective LLM collaboration approaches, such as “ Proxy Tuning, ” need all of their component models to be trained similarly. Additionally, this baseline requires each model to be used simultaneously to produce the answer, whereas MIT’s algorithm simply activates its expert model for particular tokens, leading to more efficient generation.

When to ask the expert

The MIT researchers’ algorithm highlights that imitating human teamwork more closely can increase accuracy in multi-LLM collaboration. To further elevate its factual precision, the team may draw from human self-correction: They’re considering a more robust deferral approach that can backtrack when the expert model doesn’t give a correct response. This upgrade would allow Co-LLM to course-correct so the algorithm can still give a satisfactory reply.

The team would also like to update the expert model (via only training the base model) when new information is available, keeping answers as current as possible. This would allow Co-LLM to pair the most up-to-date information with strong reasoning power. Eventually, the model could assist with enterprise documents, using the latest information it has to update them accordingly. Co-LLM could also train small, private models to work with a more powerful LLM to improve documents that must remain within the server. “Co-LLM presents an interesting approach for learning to choose between two models to improve efficiency and performance,” says Colin Raffel, associate professor at the University of Toronto and an associate research director at the Vector Institute, who wasn’t involved in the research. “Since routing decisions are made at the token-level, Co-LLM provides a granular way of deferring difficult generation steps to a more powerful model. The unique combination of model-token-level routing also provides a great deal of flexibility that similar methods lack. Co-LLM contributes to an important line of work that aims to develop ecosystems of specialized models to outperform expensive monolithic AI systems.”

Shen wrote the paper with four other CSAIL affiliates: PhD student Hunter Lang ’17, MEng ’18; former postdoc and Apple AI/ML researcher Bailin Wang; MIT assistant professor of electrical engineering and computer science Yoon Kim, and professor and Jameel Clinic member David Sontag PhD ’10, who are both part of MIT-IBM Watson AI Lab. Their research was supported, in part, by the National Science Foundation, The National Defense Science and Engineering Graduate (NDSEG) Fellowship, MIT-IBM Watson AI Lab, and Amazon. Their work was presented at the Annual Meeting of the Association for Computational Linguistics.

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