We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| chicagohai/hypothesis_generation | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| load_dict | Ran | chicagohai/hypothesis_generation/hypothesis_agent/data_analysis_agent/union_generation.py code served (permissive licence) · get_code("2a94e8d6e4a50d36") |
| load_dict | Ran | chicagohai/hypothesis_generation/hypogenic_cmd/generation.py code served (permissive licence) · get_code("3464816c127a24ac") |
| extract_hypotheses | Not yet run | chicagohai/hypothesis_generation/hypogenic/algorithm/generation/utils.py code served (permissive licence) · get_code("413bc88635e70c3d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
There is growing interest in hypothesis generation with large language models (LLMs). However, fundamental questions remain: what makes a good hypothesis, and how can we systematically evaluate methods for hypothesis generation? To address this, we introduce HypoBench, a novel benchmark designed to evaluate LLMs and hypothesis generation methods across multiple aspects, including practical utility, generalizability, and hypothesis discovery rate. HypoBench includes 7 real-world tasks and 5 synthetic tasks with 194 distinct datasets. We evaluate four state-of-the-art LLMs combined with six existing hypothesis-generation methods. Overall, our results suggest that existing methods are capable of discovering valid and novel patterns in the data. However, the results from synthetic datasets indicate that there is still significant room for improvement, as current hypothesis generation methods do not fully uncover all relevant or meaningful patterns. Specifically, in synthetic settings, as task difficulty increases, performance significantly drops, with best models and methods only recovering 38.8% of the ground-truth hypotheses. These findings highlight challenges in hypothesis generation and demonstrate that HypoBench serves as a valuable resource for improving AI systems designed to assist scientific discovery.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2504.11524")
get_code_for_paper("2504.11524")
have("2504.11524")
Connect an agent — have() is free.