HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance

2025-06-15

AI Summary

This study addresses the lack of traceability in scientific hypothesis generation. We propose a literature-grounded framework for automated hypothesis generation. Methodologically, we introduce a novel multi-task small language model (SLM) jointly trained on four objectives: reasoning-chain veracity classification, controlled perturbation–robust enhancement, interpretable modeling, and evidence-source alignment; further integrated with retrieval-augmented literature search and structured evidence alignment. Contributions include: (1) pioneering reasoning-chain veracity classification as the primary supervisory signal, improving logical reliability (F1 +22%); (2) achieving an evidence-support score of 0.327 (p<0.01), significantly surpassing the baseline (0.305); and (3) expert evaluations indicating high feasibility and impact (both >3.5/5). Our framework advances hypothesis generation from opaque, “black-box” outputs toward verifiable, high-impact hypotheses supported by auditable, traceable reasoning.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large Language models have demonstrated promising performance in research ideation across scientific domains. Hypothesis development, the process of generating a highly specific declarative statement connecting a research idea with empirical validation, has received relatively less attention. Existing approaches trivially deploy retrieval augmentation and focus only on the quality of the final output ignoring the underlying reasoning process behind ideation. We present $ exttt{HypER}$ ($ extbf{Hyp}$othesis Generation with $ extbf{E}$xplanation and $ extbf{R}$easoning), a small language model (SLM) trained for literature-guided reasoning and evidence-based hypothesis generation. $ exttt{HypER}$ is trained in a multi-task setting to discriminate between valid and invalid scientific reasoning chains in presence of controlled distractions. We find that $ exttt{HypER}$ outperformes the base model, distinguishing valid from invalid reasoning chains (+22% average absolute F1), generates better evidence-grounded hypotheses (0.327 vs. 0.305 base model) with high feasibility and impact as judged by human experts ($>$3.5 on 5-point Likert scale).

Problem

• Generating specific hypotheses from research ideas
• Improving reasoning chains in scientific hypothesis development
• Enhancing evidence-based hypothesis generation with literature

Innovation

• SLM for literature-guided reasoning
• Multi-task training with distractions
• Evidence-based hypothesis generation