Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering

arXiv:2404.14467v1 Announce Type: new
Abstract: 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 …

abstract arxiv benchmark chemistry cs.ai cs.cl dataset domain domains engineering integration knowledge language language models large language large language models llms molecules paper performance prompt scientific small study type via