Curriculum Model Merging: Harmonizing Expert Chemical LLMs for Enhanced Cross-Task Generalization
Baoyi He, Luotian Yuan, Ying Wei, Fei Wu
The emergence of large language models (LLMs) prompts fine-tuning foundation LLMs to solve real-world chemical problems. However,  these chemical LLMs are tailored to specific task formats or narrow content domains, which limits their capacity for comprehensive knowledge integration and cross-task generalization.   Model merging has recently been proposed as a promising solution and demonstrated promising performance in the domain of natural language processing (NLP), enabling the integration of multiple expert LLMs into a unified model without   requiring access to original training data or incurring significant computational costs.   The widespread prevalence of in-house training data in the chemical domain underscores the need for practical and privacy-preserving model merging method for chemical LLM integration.  % Although existing model merging methods %attempt to %minimize  %harmonize interference among models and   % have ,   However, two key characteristics of the chemical domain hinder effective model merging: 1) significant disparities among differentiated LLMs due to task-specific specializations, and 2) a highly imbalanced distribution of downstream functionality, with sparse model coverage for niche tasks and a surplus of models targeting widely studied problems.   These factors intensify model inconsistencies, such as parameter interference and accumulated fine-tuning noise, ultimately degrade performance when integrating heterogeneous chemical LLMs.  To this end, we propose Curriculum Model Merging (CMM), a method that constructs curriculum to progressively merge the expert chemical LLMs in a moderate and continual manner, aiming to harmonize the inconsistencies and mitigate the interference.   Our comprehensive experiments on two benchmark datasets show that our proposed method concentrates task-specific expertise and outperforms the state-of-the-art methods by 29.03\% in terms of an overall average performance score.   Moreover, CMM facilitates chemical knowledge generalization across prediction and generative tasks without sacrificing robustness, exhibiting promising merging performance under both expert-abundant and expert-sparse scenarios.