ArticleProceedings of the National Academy of Sciences of the United States of America2025
How should the advancement of large language models affect the practice of science?
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Article
- Subcategory vs category fluency: Items and networks in healthy young adults and simulation with a large language model.Memory & cognition · 2026Article
- The unspoken tensions that AI brings to academic collaboration.Nature human behaviour · 2026Article
- Large language models accurately identify decision reasons in verbal reports.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The sophist in the server : Rhetoric, Reasoning and Scientific Judgment in the Age of LLMs.EMBO reports · 2026Article
- Language-based assessments can predict psychological and subjective well-being.Communications psychology · 2026Article
- The "machinal bypass" and how we're using AI to avoid ourselves.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Dramatic increases in redundant publications in the Generative AI era.BMC medicine · 2025Article
- Counterfeit judgments in large language models.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Unequal Scientific Recognition in the Age of LLMs.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
- The simulation of judgment in LLMs.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cyber-Creativity: A Decalogue of Research Challenges.Journal of Intelligence · 2025Article
- High variability in LLMs' analogical reasoning.Nature human behaviour · 2025Article
- The benefits and dangers of anthropomorphic conversational agents.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Generalization bias in large language model summarization of scientific research.Royal Society open science · 2025Article
- Gen AI and research integrity: Where to now? : The integration of Generative AI in the research process challenges well-established definitions of research integrity.EMBO reports · 2025Article
- Dialogues about the practice of science.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Automating the practice of science: Opportunities, challenges, and implications.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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18 authors.
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Abstract
Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of this integration. How should the advancement of large language models affect the practice of science? For this opinion piece, we have invited four diverse groups of scientists to reflect on this query, sharing their perspectives and engaging in debate. Schulz et al. make the argument that working with LLMs is not fundamentally different from working with human collaborators, while Bender et al. argue that LLMs are often misused and overhyped, and that their limitations warrant a focus on more specialized, easily interpretable tools. Marelli et al. emphasize the importance of transparent attribution and responsible use of LLMs. Finally, Botvinick and Gershman advocate that humans should retain responsibility for determining the scientific roadmap. To facilitate the discussion, the four perspectives are complemented with a response from each group. By putting these different perspectives in conversation, we aim to bring attention to important considerations within the academic community regarding the adoption of LLMs and their impact on both current and future scientific practices.
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