HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance
This addresses the need for automated hypothesis development in scientific research, though it appears incremental by building on existing retrieval-augmented methods with a focus on reasoning processes.
The paper tackles the problem of generating specific, evidence-based scientific hypotheses using literature-grounded reasoning, and shows that their model outperforms the base model with improvements like a 22% absolute F1 gain in distinguishing valid reasoning chains and better hypothesis quality scores.
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 $\texttt{HypER}$ ($\textbf{Hyp}$othesis Generation with $\textbf{E}$xplanation and $\textbf{R}$easoning), a small language model (SLM) trained for literature-guided reasoning and evidence-based hypothesis generation. $\texttt{HypER}$ is trained in a multi-task setting to discriminate between valid and invalid scientific reasoning chains in presence of controlled distractions. We find that $\texttt{HypER}$ 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).