Reed Scott M
Department of Chemistry, University of Colorado Denver, 1151 Arapahoe St., Denver, Colorado 80217-3364, United States.
J Chem Inf Model. 2025 May 12;65(9):4274-4280. doi: 10.1021/acs.jcim.4c02322. Epub 2025 Apr 22.
Utilizing large Language models (LLMs) for handling scientific information comes with risk of the outputs not matching expectations, commonly called hallucinations. To fully utilize LLMs in research requires improving their accuracy, avoiding hallucinations, and extending their scope to research topics outside their direct training. There is also a benefit to getting the most accurate information from an LLM at the time of inference without having to create and train custom new models for each application. Here, augmented generation and machine learning-driven prompt optimization are combined to extract performance improvements over base LLM function on a common chemical research task. Specifically, an LLM was used to predict the topological polar surface area (TPSA) of molecules. By using augmented generation and machine learning-optimized prompts, the error in the prediction was reduced to 11.76 root-mean-squared error (RMSE) from 62.34 RMSE with direct calls to the same LLM.
利用大语言模型（LLMs）处理科学信息存在输出与预期不符的风险，通常称为幻觉。要在研究中充分利用大语言模型，需要提高其准确性、避免幻觉，并将其范围扩展到直接训练之外的研究主题。在推理时从大语言模型获取最准确的信息也有好处，而不必为每个应用创建和训练定制的新模型。在这里，增强生成和机器学习驱动的提示优化相结合，以在一个常见的化学研究任务上提取比基础大语言模型功能更高的性能提升。具体来说，使用一个大语言模型来预测分子的拓扑极性表面积（TPSA）。通过使用增强生成和机器学习优化的提示，预测误差从直接调用同一大语言模型时的62.34均方根误差（RMSE）降至11.76 RMSE。
