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Recent advances in Large Language Models (LLMs) have presented newopportunities for integrating Artificial General Intelligence (AGI) intobiological research and education. This study evaluated the capabilities ofleading LLMs, including GPT-4, GPT-3.5, PaLM2, Claude2, and SenseNova, inanswering conceptual biology questions. The models were tested on a108-question multiple-choice exam covering biology topics in molecular biology,biological techniques, metabolic engineering, and synthetic biology. Among themodels, GPT-4 achieved the highest average score of 90 and demonstrated thegreatest consistency across trials with different prompts. The resultsindicated GPT-4's proficiency in logical reasoning and its potential to aidbiology research through capabilities like data analysis, hypothesisgeneration, and knowledge integration. However, further development andvalidation are still required before the promise of LLMs in acceleratingbiological discovery can be realized.

Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions