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LLM4SR: A Survey on Large Language Models for Scientific Research

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arxiv 2501.04306 v1 pith:UHUYKZA5 submitted 2025-01-08 cs.CL cs.DL

Ziming Luo , Zonglin Yang , Zexin Xu , Wei Yang , Xinya Du This is my paper
classification cs.CLcs.DL
keywords researchllmsscientificsurveyacrosslanguagelargellm4sr
verification ladder T0 review T1 audit T2 compute T3 formal
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In recent years, the rapid advancement of Large Language Models (LLMs) has transformed the landscape of scientific research, offering unprecedented support across various stages of the research cycle. This paper presents the first systematic survey dedicated to exploring how LLMs are revolutionizing the scientific research process. We analyze the unique roles LLMs play across four critical stages of research: hypothesis discovery, experiment planning and implementation, scientific writing, and peer reviewing. Our review comprehensively showcases the task-specific methodologies and evaluation benchmarks. By identifying current challenges and proposing future research directions, this survey not only highlights the transformative potential of LLMs, but also aims to inspire and guide researchers and practitioners in leveraging LLMs to advance scientific inquiry. Resources are available at the following repository: https://github.com/du-nlp-lab/LLM4SR

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  4. ScienceMeter: Tracking Scientific Knowledge Updates in Language Models

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    ScienceMeter evaluates language model knowledge updates across three axes, preservation of old scientific claims, acquisition of new claims, and projection to future findings, and finds all current methods fall short.

  5. Towards Fully Automated Molecular Simulations: Multi-Agent Framework for Simulation Setup and Force Field Extraction

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