| Challenge: | Model merging is an efficient way of obtaining a multi-task model from several pretrained models without further fine-tuning. |
| Approach: | They propose a model merging technique that aims at mitigating "merging conflicts" by truncating small components in the respective spectral spaces and then an automatic parameter rescaling scheme to retain the nuclear norm of the original matrix. |
| Outcome: | The proposed model outperforms baseline models on flan-T5 by 4.2% and is robust to hyperparamater choice. |
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| Challenge: | a framework for model merging is proposed without additional training . task vectors from fine-tuned models exhibit a limited number of dominant singular values . |
| Approach: | They propose a framework for model merging based on low-rank estimation of task vectors without access to the base model. |
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LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint (2025.acl-long)
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| Challenge: | Existing methods for fine-tuning large language models for specialized tasks are costly and time-consuming. |
| Approach: | They propose a framework that locates task-specific neurons via gradient-based attribution and dynamically Elects critical neurons through multi-model importance fusion. |
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DivMerge: A divergence-based model merging method for multi-tasking (2026.eacl-long)
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| Challenge: | Existing methods for multitask learning struggle with interference between tasks, especially as the number of tasks grows. |
| Approach: | They propose a reference-free method that minimizes the divergence between models' outputs and those of the merged model, automatically balancing task importance. |
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Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large Language Models (2025.findings-acl)
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| Challenge: | Existing task vector-based model merging methods apply uniform coefficients across all parameters, overlooking varying parameter importance both within and across tasks. |
| Approach: | They propose a sensitivity-guided coefficient adjustment method that optimizes existing model merging techniques by operating at both task-specific and cross-task levels. |
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Unraveling LoRA Interference: Orthogonal Subspaces for Robust Model Merging (2025.acl-long)
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| Challenge: | Existing methods for fine-tuning large language models fail due to performance degradation . existing methods fail for models fine- tuned with low-rank adaptation . |
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PRIME: Ultra-Low-Rank Principal–Residual Model Merging (2026.findings-acl)
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Seung-Ho Lee, Kyungsu Lee, Bazarvaani Zuchi, Jeongmin Ahn, Insuk Seo, Donghyeon Jeon, Inho Kang, Seung-Hoon Na
| Challenge: | Existing methods for model merging have been limited by task-specific performance and task-related tasks. |
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Practical Guidelines for Model Merging in LLMs Pre-Training (2026.acl-industry)
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| Challenge: | Existing studies on model merging have focused on stable learning rate regimes, but its effectiveness during LLM pre-training remains underexplored. |
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LM-Cocktail: Resilient Tuning of Language Models via Model Merging (2024.findings-acl)
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| Challenge: | Pre-trained language models are continually fine-tuned to better support downstream applications. however, this operation may result in significant performance degeneration on general perspectives. |
| Approach: | They propose a method which enables pre-trained language models to stay resilient in general perspectives. |
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To See a World in a Spark of Neuron: Disentangling Multi-Task Interference for Training-Free Model Merging (2025.emnlp-main)
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Zitao Fang, Guodong Du, Shuyang Yu, Yifei Guo, Yiwei Zhang, Yiyao Cao, Jing Li, Ho-Kin Tang, Sim Kuan Goh
| Challenge: | Existing approaches to model merging ignore the fundamental roles of neurons, connectivity and activation. |
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Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models (2025.acl-long)
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| Challenge: | Existing approaches to align large language models with information extraction tasks are costly and not all training data benefits target domains. |
| Approach: | They propose a framework which dynamically Selects and Merges expert models at inference time and combines experts beneficial to target domains. |
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