LLM4SR: A Survey on Large Language Models for Scientific Research

Abstract: In recent years, the rapid advancement of 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

• The paper presents a systematic survey of LLM applications across the scientific research pipeline, from hypothesis discovery to peer review.
• It details innovative methodologies including agent-based design for experiment planning and retrieval-augmented techniques for enhancing scientific writing.
• The paper underscores challenges such as factual accuracy and ethical concerns, and discusses future directions for automated research workflows.

LLM4SR: A Survey on LLMs for Scientific Research

This survey paper (2501.04306) presents a comprehensive overview of how LLMs are impacting the scientific research landscape. It categorizes the applications of LLMs across the scientific research pipeline, which includes hypothesis discovery, experiment planning and implementation, scientific writing, and peer reviewing. It provides a structured analysis of methodologies, benchmarks, and evaluation techniques associated with each stage, offering insights into the potential and limitations of LLMs in scientific research.

Scientific Hypothesis Discovery with LLMs

This section reviews the use of LLMs in scientific hypothesis discovery, contrasting them with traditional methods like literature-based discovery (LBD) and inductive reasoning. LBD, pioneered by Swanson, focuses on uncovering relationships between disparate pieces of knowledge within existing literature. Inductive reasoning, on the other hand, involves deriving general rules from specific observations. The survey highlights how LLMs are used to bridge these approaches, offering capabilities such as retrieving relevant knowledge, identifying novel connections, and ensuring the validity and clarity of generated hypotheses. Various methods, including those using evolutionary algorithms and multiple inspirations, are detailed, providing a view on how LLMs can contribute to the scientific discovery process.

Figure 1: Schematic overview of the scientific research pipeline covered in this survey, highlighting the cyclical nature of scientific research, from hypothesis discovery to peer review.

A key component in this area is the iterative feedback mechanism focused on novelty, validity, and clarity. Novelty is often assessed using LLMs to compare generated hypotheses against existing literature or leveraging the internal knowledge of the LLM itself. Validity checking, ideally performed through real experiments, currently relies on heuristics or trained neural models, though the survey points to future directions involving robotics and automated labs. Clarity, essential for detailed hypotheses, is typically evaluated through LLM self-assessment.

LLMs for Experiment Planning and Implementation

This section addresses the role of LLMs in automating and optimizing experiment design and implementation. It highlights the agent-based design of LLMs, emphasizing their modularity and tool integration capabilities. This allows LLMs to seamlessly interact with external systems such as databases, experimental platforms, and computational tools. LLMs assist in task decomposition, where experiments are broken down into manageable sub-tasks, enhancing efficiency and alignment with research goals.

The discussion extends to the automation of experimental processes, including data preparation, experiment execution, and data analysis. LLMs can streamline data preparation tasks such as cleaning, labeling, and feature engineering, particularly when dealing with large datasets. LLMs can also synthesize experimental data, useful in fields like social science where experiments with human subjects are constrained. Automating experiment execution involves pretraining, fine-tuning, and tool-augmented learning, enabling LLMs to acquire task-specific capabilities.

Automating Scientific Paper Writing

The paper writing stage is analyzed, covering citation text generation, related work generation, and drafting. LLMs enhance citation text generation by providing contextual understanding and coherence, using techniques like pointer-generator networks and multimodal approaches. In related work generation, LLMs are used to create comprehensive literature reviews, leveraging their ability to handle extensive input lengths and provide nuanced contextual understanding. Techniques like Retrieval-Augmented Generation (RAG) are used to ground LLMs in factual content, minimizing hallucinations and improving accuracy. LLMs are also used in drafting and writing, generating specific textual elements such as figure captions or drafting entire research papers, contributing to the overall efficiency of the scientific writing process.

LLM-Assisted Peer Review

The final section focuses on the integration of LLMs in peer review, presenting automated review generation and LLM-assisted review workflows. Automated review generation explores using LLMs to produce comprehensive reviews with minimal human intervention, employing single-model and multi-model architectures. LLM-assisted review workflows focus on enhancing human reviewers' capabilities, where LLMs assist in tasks such as information extraction, manuscript validation, and review writing. These workflows enhance efficiency while maintaining human oversight in the evaluation process.

Conclusion

The survey concludes by recognizing the transformative potential of LLMs in scientific research, emphasizing the need to address challenges such as factual accuracy, contextual coherence, and ethical considerations. The integration of LLMs into scientific workflows promises to accelerate discoveries and foster unprecedented innovation and collaboration in the scientific community.