LLM use in scholarly writing poses a provenance problem

Building Scholar-Ready AI: A Conversation with Todd Toler - The Scholarly Kitchen

A conversation on AI retrieval, the provenance problem, and the shared infrastructure scholarly publishing needs.

The AI Chemist: To be trustworthy, LLMs need to show their work

Good scientists reveal how they do their experiments and report their results; so should any machine-driven research

Attribution, Provenance, Reference, Citation, and AI for Research Applications – Understanding the Differences - The Scholarly Kitchen

Building robust citation and attribution into generative AI systems are foundational to usage, credit and trust. We need to expect more from AI.

No. 013: Provenance vocabulary, Wikipedia contamination, OpenAI rare-disease diagnoses, ARA paper format - The Hallway Track

The Hallway Track No. 013 - How AI is affecting the practice of science

Guest Post — When Errors Become Consensus: Science’s Self-Correction Can No Longer Keep Up - The Scholarly Kitchen

From 19th-century beef tea to paper mills and generative AI, the challenge is no longer detection alone; it is preventing bad claims from becoming embedded knowledge.

Artificial intelligences and human scientists exhibit...

We investigate the effectiveness of artificial intelligences (AI)-specifically large language models (LLMs)-relative to human scientists at high-level cognitive tasks in social science such as...

How Do We Talk about LLMs?

By Jim Clifford It is never fun to watch friends argue. Generative AI has created multiple fractures across our universities and the community of historians. The stakes are high and we are all deal…

AI for Research | Scite

Scite searches 280M+ scholarly articles to give you citation-backed answers and show you how every claim is supported or disputed.

Bending the Curve: How AI is Transforming Scientific Discovery

Discover how researchers use LLMs and specialized AI models like AlphaFold as complementary tools to accelerate innovation and expand the scientific frontier.

Unequal Scientific Recognition in the Age of LLMs

Large language models (LLMs) are reshaping how scientific knowledge is accessed and represented. This study evaluates the extent to which popular and frontier LLMs including GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro recognize scientists, benchmarking their outputs against OpenAlex and Wikipedia. Using a dataset focusing on 100,000 physicists from OpenAlex to evaluate LLM recognition, we uncover substantial disparities: LLMs exhibit selective and inconsistent recognition patterns. Recognition correlates strongly with scholarly impact such as citations, and remains uneven across gender and geography. Women researchers, and researchers from Africa, Asia, and Latin America are significantly underrecognized. We further examine the role of training data provenance, identifying Wikipedia as a potential sources that contributes to recognition gaps. Our findings highlight how LLMs can reflect, and potentially amplify existing disparities in science, underscoring the need for more transparent and inclusive knowledge systems.

Elsevier vs Meta: first science publisher sues over scraped research papers

Science publishing giant Elsevier has joined a class-action lawsuit against Meta that alleges the reproduction of copyrighted works in developing the Llama AI model.

Elsevier vs Meta: first science publisher sues over scraped research papers

Science publishing giant Elsevier has joined a class-action lawsuit against Meta that alleges the reproduction of copyrighted works in developing the Llama AI model.

AI Use in The Scholarly Kitchen - The Scholarly Kitchen

Some thoughts on the reader response the Scholarly Kitchen has seen regarding AI use by authors, a recent Nature News article on the efficacy of AI detection tools, and why it's important to keep experimenting to figure out what's ethically acceptable.

Illicit Use of AI by Philosophers Refereeing for Journals - Daily Nous

In 2024, a study found that “7–17% of the sentences in the reviews [of computer science manuscripts] were written by LLMs”. It was only a […]

Dan Shipper 📧 on Twitter / X

this is true and is a big reason why you don’t need to be a highly technical researcher to use LLMs in surprising and novel ways https://t.co/TuxNzXzToU— Dan Shipper 📧 (@danshipper) July 27, 2025

Scientific production in the era of Large Language Models

Large Language Models (LLMs) are rapidly reshaping scientific research. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and 246M online accesses to scientific documents. We find: 1) scientists adopting LLMs to draft manuscripts demonstrate a large increase in paper production, ranging from 23.7-89.3% depending on scientific field and author background, 2) LLM use has reversed the relationship between writing complexity and paper quality, leading to an influx of manuscripts that are linguistically complex but substantively underwhelming, and 3) LLM adopters access and cite more diverse prior work, including books and younger, less-cited documents. These findings highlight a stunning shift in scientific production that will likely require a change in how journals, funding agencies, and tenure committees evaluate scientific works.