The emergence of large language models (LLMs) has opened new opportunities for AI-driven chemical problem solving. However, existing chemical LLMs are typically tailored to specific task formats or narrow domains, limiting their capacity to integrate knowledge and generalize across tasks. Model merging offers a promising route for efficiently combining specialized LLMs into a unified model without access to original training data, which is urgently needed in the chemical domain where in-house data and privacy preservation are critical. However, effective model merging in the chemical domain poses unique challenges: (1) significant disparities among chemical LLMs due to task-specific specialization, and (2) a highly imbalanced distribution of chemical LLMs in targeted downstream tasks, where some are over-benchmarked while others remain underexplored. These challenges intensify model inconsistencies such as parameter interference and accumulated fine-tuning noise, which collectively hinder effective model merging. To this end, we propose Curriculum Model Merging (CMM), a curriculum-based framework that progressively merges expert chemical LLMs in a moderate and continual manner. CMM aims to harmonize their inconsistencies while meantime preserve their domain-specific expertise. Comprehensive experiments on two benchmark datasets show that CMM effectively consolidates task-specific expertise and outperforms the state-of-the-art methods by 29.03\% in terms of overall average performance. 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.

Yuqing Zhang, Baoyi He, Yihan Chen, Hangqi Li, Han Yue, Shengyu Zhang, Huaiyong Dou, Junchi Yan, Zemin Liu, Yongquan Zhang, Fei Wu. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, Fei Wu. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

The rich tapestry of human history is often painstakingly pieced together from ancient manuscripts, serving as resilient time capsules of cultural heritage, societal norms, religious tenets, and quotidian life. Unfortunately, the ravages of time, careless preservation, and varied forms of degradation frequently leave us with fragmented relics. The traditional process of reconstructing these fragments is an arduous task, demanding exhaustive manual intervention and a global collaboration among archaeologists. This paper presents a transformative approach to this challenge, harnessing multi-media techniques to restore the connectable fragments of the invaluable Dunhuang scrolls. We curate a unique multimodal dataset of the fragmented Dunhuang manuscripts and architect an innovative three-tiered pipeline to reconstruct these historical scrolls. Our initial stage uses a text-based localization strategy, filtering fragment pairs through text comparison. We then employ a novel self-supervised contour-based pairwise matching framework to overcome the hurdle of limited labeled pairing samples. This process is powered by data augmentation techniques and a Siamese network which determines the most compatible matches. The final stage in our pipeline globally reconstructs the selected fragment pairs with hierarchical clustering, bringing us closer to the original grandeur of the Dunhuang scrolls. Our empirical evaluations reveal that this pipeline exhibits a remarkable success rate in fragment assembly. By addressing this cross-disciplinary challenge, our dataset and pipeline not only contribute to the field of multi-media artificial intelligence but also hold profound implications for sociocultural studies and future explorations into the understanding of ancient cultural history.