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

Botao Yu, Frazier N. Baker, Ziqi Chen, Xia Ning, Huan Sun

Research Area: Data, Evaluation, LMs on diverse modalities and novel applications

Keywords: chemistry, LLMs, fine-tuning, instruction tuning, dataset, molecule

TL;DR: Our LLMs that can achieve very strong results on chemistry tasks, outperforming GPT-4 and Claude 3 Opus by a substantial margin and approaching the SoTA task-specific models.

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 research indicates that 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 and Claude 3 Opus by a substantial margin. To accomplish this, we propose SMolInstruct, a large-scale, comprehensive, and high-quality dataset for instruction tuning. It contains 14 selected chemistry tasks and over three million samples, laying a solid foundation for training and evaluating LLMs for chemistry. Using SMolInstruct, we fine-tune a set of open-source LLMs named as LlaSMol, among which, we find that Mistral serves as the best base model for chemistry tasks. Our analysis further demonstrates the critical role of the proposed dataset in driving the performance improvements.

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Submission Number: 368