Large Language Models for Hypothesis Generation and Scientific Discovery: Methods, Challenges, and Future Directions.

The emergence of Large Language Models (LLMs) has substantially expanded the potential role of artificial intelligence in scientific research. Beyond applications in text generation, summarization, and question answering, LLMs are increasingly being investigated for scientific knowledge synthesis, reasoning, hypothesis generation, and research automation. Their ability to process large volumes of scientific information and connect knowledge across domains offers new opportunities for identifying potential relationships, generating candidate hypotheses, and supporting the exploration of complex research problems. However, their application to scientific discovery remains constrained by hallucinations, factual inaccuracies, limited transparency, reproducibility challenges, bias, and uncertainty regarding the scientific validity and novelty of generated hypotheses. This review examines the evolving role of LLMs in scientific discovery, with particular emphasis on scientific hypothesis generation. We synthesize current approaches to LLM-based hypothesis generation, including prompt-based, iterative, retrieval-augmented, multi-agent, and tool-augmented methods, and examine their applications across scientific domains. We further discuss approaches for evaluating generated hypotheses, major technical and methodological challenges, and emerging directions involving human AI collaboration and autonomous scientific discovery. Overall, the review highlights the potential of LLMs to expand the space of scientific hypotheses while emphasizing that their contribution to scientific knowledge ultimately depends on rigorous evaluation, computational or experimental validation, and responsible integration into scientific workflows