LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset

arXiv:2402.09391v1 Announce Type: cross
Abstract: Chemistry plays a crucial role in many domains, such as drug discovery and material science. While large language models (LLMs) such as GPT-4 exhibit remarkable capabilities on natural language processing tasks, existing work shows their performance on chemistry tasks is discouragingly low. In this paper, however, we demonstrate that our developed LLMs can achieve very strong results on a comprehensive set of chemistry tasks, outperforming the most advanced GPT-4 across all the tasks by a …

abstract arxiv capabilities chemistry cs.ai cs.ce cs.cl dataset discovery domains drug discovery gpt gpt-4 language language models language processing large language large language models llms material natural natural language natural language processing performance processing quality role scale science shows tasks type work