HypoBench is a systematic and principled benchmark designed to evaluate the hypothesis generation capabilities of AI systems, particularly Large Language Models (LLMs).

What is HypoBench?

HypoBench provides a comprehensive framework for assessing how well models can generate plausible hypotheses that explain observed phenomena. Our benchmark:

• Combines real-world and synthetic datasets across 12 domains and 194 datasets
• Evaluates multiple dimensions of hypothesis quality, with emphasis on explanatory power
• Enables systematic comparison between different methods and models

Why Hypothesis Generation?

Hypothesis generation is ubiquitous in scientific discoveries and everyday reasoning. It involves proposing natural language theories or explanations about observed phenomena, requiring key capabilities like:

• Inductive reasoning
• Abstraction and clear communication
• Synthesis of information

Key Findings

Our experiments have revealed several important insights:

• Data-driven hypothesis generation methods outperform both zero-shot and few-shot inference approaches
• Combining literature with data for hypothesis generation achieves the best performance
• Current methods struggle with more complex hypothesis generation tasks, with performance dropping as difficulty increases
• There remains substantial room for improvement in this important area

Get Started

• Explore our datasets
• Learn about our evaluation methods
• Check the leaderboards

HypoBench is an ongoing project aimed at advancing hypothesis generation capabilities in AI systems. We welcome contributions and feedback from the research community.

For more details, please refer to our paper.