HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916911679275008 |
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| author | Vasu, Rosni Basu, Chandrayee Mishra, Bhavana Dalvi Sarasua, Cristina Clark, Peter Bernstein, Abraham |
| author_facet | Vasu, Rosni Basu, Chandrayee Mishra, Bhavana Dalvi Sarasua, Cristina Clark, Peter Bernstein, Abraham |
| contents | 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). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_12937 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance Vasu, Rosni Basu, Chandrayee Mishra, Bhavana Dalvi Sarasua, Cristina Clark, Peter Bernstein, Abraham Artificial Intelligence Computation and Language 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). |
| title | HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2506.12937 |