This paper presents a study on the integration of domain-specific knowledge
in prompt engineering to enhance the performance of large language models
(LLMs) in scientific domains. A benchmark dataset is curated to encapsulate the
intricate physical-chemical properties of small molecules, their drugability
for pharmacology, alongside the functional attributes of enzymes and crystal
materials, underscoring the relevance and applicability across biological and
chemical domains.The proposed domain-knowledge embedded prompt engineering
method outperforms traditional prompt engineering strategies on various
metrics, including capability, accuracy, F1 score, and hallucination drop. The
effectiveness of the method is demonstrated through case studies on complex
materials including the MacMillan catalyst, paclitaxel, and lithium cobalt
oxide. The results suggest that domain-knowledge prompts can guide LLMs to
generate more accurate and relevant responses, highlighting the potential of
LLMs as powerful tools for scientific discovery and innovation when equipped
with domain-specific prompts. The study also discusses limitations and future
directions for domain-specific prompt engineering development.

本研究探讨了将领域特定知识整合到提示工程中，以增强科学领域的大型语言模型（LLMs）的性能。通过构建基准数据集，该方法在生物和化学领域中的复杂材料上得到验证，结果表明领域知识提示可以引导 LLMs 生成更准确和相关的回答，凸显 LLMs 在科学发现和创新中的潜力。