🔗 Who cites «AdaMerging: Adaptive Model Merging for Multi-Task Learning»

54 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «AdaMerging: Adaptive Model Merging for Multi-Task Learning»

1. Mitigating the Alignment Tax of RLHFConference on Empirical Methods in Natural Language Processing (EMNLP)Yong Lin, Hangyu Lin, Wei Xiong, et al.2024 · 83 citations
2. Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and OpportunitiesarXiv:2408.07666Enneng Yang, Li Shen, Guibing Guo, et al.2024 · 79 citations
3. Nature-Inspired Population-Based Evolution of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yiqun Zhang, Peng Ye, Xiaocui Yang, et al.2026 · 8 citations
4. Revisiting Model Interpolation for Efficient ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Taiqiang Wu, Runming Yang, Tao Liu, et al.2026 · 6 citations
5. LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-DisjointAnnual Meeting of the Association for Computational Linguistics (ACL)Qianli Ma, Dongrui Liu, Qian Chen, et al.2025 · 5 citations
6. Why Do More Experts Fail? A Theoretical Analysis of Model MergingAnnual Meeting of the Association for Computational Linguistics (ACL)Zijing Wang, Xingle Xu, YongKang Liu, et al.2026 · 2 citations
7. DivMerge: A divergence-based model merging method for multi-taskingConference of the European Chapter of the Association for Computational Linguistics (EACL)Brahim Touayouch, Loïc Fosse, Géraldine Damnati, et al.2026
8. ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model MergingAnnual Meeting of the Association for Computational Linguistics (ACL)Junyao Yang, Chen Qian, Wen Shen, et al.2026

46 more papers of our library cite it; they have no page here yet.

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