Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions

TL;DR

This survey reviews how Large Language Models are revolutionizing scientific hypothesis generation and validation through various methods, datasets, and future research directions, emphasizing interpretability, domain adaptation, and ethical considerations.

Contribution

It provides a comprehensive overview of LLM-driven approaches, compares symbolic and modern pipelines, introduces new datasets, and outlines future research roadmap for scientific discovery.

Findings

LLMs enable advanced hypothesis synthesis and validation techniques.

New datasets like AHTech and CSKG-600 support scientific research.

Future directions include multimodal integration and ethical safeguards.

Abstract

Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmentation. This survey provides a structured overview of LLM-driven approaches, including symbolic frameworks, generative models, hybrid systems, and multi-agent architectures. We examine techniques such as retrieval-augmented generation, knowledge-graph completion, simulation, causal inference, and tool-assisted reasoning, highlighting trade-offs in interpretability, novelty, and domain alignment. We contrast early symbolic discovery systems (e.g., BACON, KEKADA) with modern LLM pipelines that leverage in-context learning and domain adaptation via fine-tuning, retrieval, and symbolic grounding. For validation, we review simulation, human-AI collaboration, causal modeling, and uncertainty quantification, emphasizing iterative assessment in open-world contexts. The survey maps datasets across biomedicine, materials science, environmental science, and social science, introducing new resources like AHTech and CSKG-600. Finally, we outline a roadmap emphasizing novelty-aware generation, multimodal-symbolic integration, human-in-the-loop systems, and ethical safeguards, positioning LLMs as agents for principled, scalable scientific discovery.