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It's completely free and we'll try out ideas for you!

10/ Model priors matter: We see that the models have different priors, which lead to varying behaviors in different tasks—generating good hypotheses is harder when prior knowledge is not helpful.

9/ And it gets worse in counterintuitive settings - the models perform significantly worse when the underlying hypotheses are counterintuitive.

8/ 💡 Synthetic dataset results show: LLMs handle simple interactions well but struggle with increased noise, distractors, or subtleties in text—highlighting significant rooms for improvement.

🚀🚀🚀Excited to share our latest work: HypoBench, a systematic benchmark for evaluating LLM-based hypothesis generation methods!

There is much excitement about leveraging LLMs for scientific hypothesis generation, but principled evaluations are missing - let’s dive into HypoBench together.

💡Check out our project website for our latest paper! Learn about a new approach to hypothesis generation:
👉 chicagohai.github.io/hypogenic-de...