The illusion of understanding that Messeri & Crockett 2024 warn could define a new phase of science now has a measured mechanism: Peters & Chin-Yee 2025 find that widely used LLMs systematically overgeneralize scientific conclusions when summarizing—explicitly risking illusions of understanding—and that newer models do this worse than earlier ones.
E8 warns of a phase of scientific enquiry in which we 'produce more but understand less,' and E36–E37 show widely used LLMs are strongly biased toward overgeneralizing scientific conclusions when summarizing—explicitly risking 'illusions of understanding'—with newer models performing worse in generalization accuracy than earlier ones.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 24 Oct, 04:56
Source chain
AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less”
Curator's annotation · p. 49 · evidence E8
The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less
Curator's note: This is one of the comments I hear most often when speaking to colleagues about use of AI tools.
- 02
“Our results indicate a strong bias in many widely used LLMs towards overgeneralizing scientific conclusions, posing a significant risk of large-scale misinterpretations of research findings”
Full-text passage · no page number · evidence E36
Notably, newer models tended to perform worse in generalization accuracy than earlier ones. Our results indicate a strong bias in many widely used LLMs towards overgeneralizing scientific conclusions, posing a significant risk of large-scale misinterpretations of research findings. We highlight potential mitigation strategies, including lowering LLM temperature settings and benchmarking LLMs for generalization accuracy. Keywords: large language models, algorithmic bias, science communication, overgeneralization Introduction Accurately communicating findings of scientific studies is vital for educating the public, informing policy, guiding behaviour, and advancing research [1,2]. To learn about, review, and communicate scientific findings, both experts (e.g. researchers) and laypeople (e.g. reporters and students) now increasingly use artificial intelligence (AI) chatbots (e.g. ChatGPT) powered by large language models (LLMs) [3–5]. AI chatbots can process vast amounts of scientific information and summarize content in easily understandable language, thus helping to spread scientific knowledge, promote evidence uptake, and facilitate research [3,6,7].
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.
- 03
“This can result in widespread misinterpretations of findings, illusions of understanding, research lacunas, and risky practices”
Full-text passage · no page number · evidence E37
However, many experts have voiced concerns, noting that AI chatbots used as science communication tools may generate plausible sounding but false or misleading information [3,8–10]. One important related yet underexplored issue is that chatbots may overlook uncertainties, limitations, and nuances in original research by omitting qualifiers and oversimplifying text [11,12], leading to overgeneralizations, i.e. generalizations that are broader than those in the original text and that may therefore be unwarranted by the original findings. This can result in widespread misinterpretations of findings, illusions of understanding, research lacunas, and risky practices [13]. For instance, LLM chatbots are increasingly used in medical education and clinical practice for research summarization and answering medical queries [10,14,15]. If chatbots produce summaries that overlook qualifiers or restrictors to the generalizability of clinical trial results, trainees and practitioners who rely on these chatbots may prescribe unsafe or inappropriate treatments.
Passage read from www.ebi.ac.uk, which may be a preprint rather than the published version.