BABE: Biology Arena BEnchmark

TL;DR

BABE is a new benchmark designed to evaluate biological AI systems' ability to perform experimental reasoning, integrating complex scientific knowledge and reasoning skills from real research data.

Contribution

It introduces a comprehensive, real-world-based benchmark for assessing experimental reasoning in biological AI, addressing a gap in existing evaluation methods.

Findings

BABE effectively measures causal reasoning and cross-scale inference.

Models show varying proficiency, highlighting areas for improvement.

The benchmark promotes development of more scientifically capable AI systems.

Abstract

The rapid evolution of large language models (LLMs) has expanded their capabilities from basic dialogue to advanced scientific reasoning. However, existing benchmarks in biology often fail to assess a critical skill required of researchers: the ability to integrate experimental results with contextual knowledge to derive meaningful conclusions. To address this gap, we introduce BABE(Biology Arena BEnchmark), a comprehensive benchmark designed to evaluate the experimental reasoning capabilities of biological AI systems. BABE is uniquely constructed from peer-reviewed research papers and real-world biological studies, ensuring that tasks reflect the complexity and interdisciplinary nature of actual scientific inquiry. BABE challenges models to perform causal reasoning and cross-scale inference. Our benchmark provides a robust framework for assessing how well AI systems can reason like practicing scientists, offering a more authentic measure of their potential to contribute to biological research.