Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions
This work assesses the potential of LLMs to assist in biology research and education, but it is incremental as it applies existing models to a new domain without novel methodological contributions.
The study evaluated leading large language models on a 108-question biology exam, finding that GPT-4 achieved the highest average score of 90 and showed strong logical reasoning capabilities.
Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education. This study evaluated the capabilities of leading LLMs, including GPT-4, GPT-3.5, PaLM2, Claude2, and SenseNova, in answering conceptual biology questions. The models were tested on a 108-question multiple-choice exam covering biology topics in molecular biology, biological techniques, metabolic engineering, and synthetic biology. Among the models, GPT-4 achieved the highest average score of 90 and demonstrated the greatest consistency across trials with different prompts. The results indicated GPT-4's proficiency in logical reasoning and its potential to aid biology research through capabilities like data analysis, hypothesis generation, and knowledge integration. However, further development and validation are still required before the promise of LLMs in accelerating biological discovery can be realized.