ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions

• URL: http://arxiv.org/abs/2509.16543v1
• Date: Sat, 20 Sep 2025 05:43:58 GMT
• Title: ChemOrch: Empowering LLMs with Chemical Intelligence via Synthetic Instructions
• Authors: Yue Huang, Zhengzhe Jiang, Xiaonan Luo, Kehan Guo, Haomin Zhuang, Yujun Zhou, Zhengqing Yuan, Xiaoqi Sun, Jules Schleinitz, Yanbo Wang, Shuhao Zhang, Mihir Surve, Nitesh V Chawla, Olaf Wiest, Xiangliang Zhang,
• Abstract summary: ChemOrch is a framework that synthesizes chemically grounded instruction-response pairs.<n>ChemOrch enables controllable diversity and levels of difficulty for the generated tasks.
• Score: 52.79349601462865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Empowering large language models (LLMs) with chemical intelligence remains a challenge due to the scarcity of high-quality, domain-specific instruction-response datasets and the misalignment of existing synthetic data generation pipelines with the inherently hierarchical and rule-governed structure of chemical information. To address this, we propose ChemOrch, a framework that synthesizes chemically grounded instruction-response pairs through a two-stage process: task-controlled instruction generation and tool-aware response construction. ChemOrch enables controllable diversity and levels of difficulty for the generated tasks, and ensures response precision through tool planning and distillation, and tool-based self-repair mechanisms. The effectiveness of ChemOrch is evaluated based on: 1) the high quality of generated instruction data, demonstrating superior diversity and strong alignment with chemical constraints; 2) the reliable generation of evaluation tasks that more effectively reveal LLM weaknesses in chemistry; and 3) the significant improvement of LLM chemistry capabilities when the generated instruction data are used for fine-tuning. Our work thus represents a critical step toward scalable and verifiable chemical intelligence in LLMs.

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