GENERATIVE AI FOR SCIENTIFIC DISCOVERY: AUTOMATED HYPOTHESIS GENERATION AND VALIDATION USING LLMS

Abstract : The rapid expansion of scientific literature presents both an opportunity and a challenge for researchers seeking to generate novel hypotheses and accelerate discovery. Generative AI, particularly transformer-based large language models (LLMs), offers a transformative approach to automate hypothesis generation and validation by mining, synthesizing, and reasoning over vast corpora of scientific texts. This study explores the development and application of an LLM-driven framework designed to extract latent patterns, generate testable hypotheses, and assist in their preliminary validation using structured knowledge and domain-specific data. We leverage state-of-the-art transformer models fine-tuned on curated scientific datasets across disciplines, enabling contextual understanding and domain relevance. The system incorporates retrieval-augmented generation to ensure grounded outputs, linking generated hypotheses to supporting evidence in peer-reviewed literature. Furthermore, we introduce a pipeline for assessing hypothesis novelty and feasibility by integrating citation analysis, semantic similarity scoring, and cross-validation against existing experimental results. A key component of our approach is the automated identification of underexplored or contradictory areas within the literature, which serves as fertile ground for hypothesis innovation. Case studies in biomedical research and materials science demonstrate the model's ability to propose insightful, previously unreported hypotheses—such as potential gene-disease associations or novel material property relationships—which are evaluated with domain expert input and, where possible, matched to ongoing research. Additionally, we explore techniques for iterative refinement of hypotheses through human-in-the-loop feedback and reinforcement learning with human preferences (RLHF), further aligning model outputs with scientific plausibility and relevance. The results suggest that LLMs, when systematically deployed with appropriate guardrails and expert oversight, can significantly augment human creativity in science by reducing the time from question formulation to experimental design. This research highlights the potential of generative AI not only as a tool for summarization and retrieval, but as a collaborator in the scientific process, capable of proposing mechanistic insights and guiding empirical exploration. Future work will focus on integrating multimodal data sources, such as experimental datasets and structured knowledge graphs, and on establishing robust benchmarks for evaluating AI-generated hypotheses across disciplines. Our findings underscore the promise of LLMs in reshaping how hypotheses are generated and validated, opening pathways toward more efficient, data-driven, and democratized scientific discovery.

Keyword Generative AI, Large Language Models, Hypothesis Generation, Scientific Literature Mining, Transformer Models, Automated Discovery, Retrieval-Augmented Generation, Human-in-the-Loop Validation