Papers with model merging techniques
Towards Robust Sentiment Analysis of Temporally-Sensitive Policy-Related Online Text (2025.acl-srw)
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| Challenge: | Existing methods fail to adequately capture the temporal volatility inherent in policy-related sentiments, arguing that continuous time-series clustering and model merging achieve superior performance. |
| Approach: | They propose to use continuous time-series clustering to select data points for annotation based on temporal trends and then apply model merging techniques. |
| Outcome: | The proposed methods outperform existing methods by an average F1-score of 2.71% on temporally representative data. |
Disperse-Then-Merge: Pushing the Limits of Instruction Tuning via Alignment Tax Reduction (2024.findings-acl)
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| Challenge: | Pre-trained language models may not follow human instructions and produce toxic, hallucinated, or biased content. |
| Approach: | They propose a disperse-then-merge framework that dispersers instruction-following data into portions and trains multiple sub-models using different data portions. |
| Outcome: | The proposed framework outperforms data curation and training regularization on standard knowledge and reasoning benchmarks. |
Model Merging and Safety Alignment: One Bad Model Spoils the Bunch (2024.findings-emnlp)
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| Challenge: | Existing methods for merging large language models often overlook safety alignment during merging, leading to misaligned models. |
| Approach: | They propose to combine safety and domain-specific data to optimize model merging techniques . they propose to use this data to maximize model alignment . |
| Outcome: | The proposed method allows for models that excel in both domain expertise and alignment. |
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. |
| Outcome: | The proposed method outperforms existing model merging techniques on mistral 7B and LLaMA2 7B/13B models and enables them to outperformed specialized models. |
LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging (2025.findings-emnlp)
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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. |
| Outcome: | The proposed framework improves models without additional training without additional inputs. |
HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging (2025.emnlp-main)
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| Challenge: | Existing methods that produce a fixed trade-off between storage size and performance are often ineffective due to the growing size of large language models. |
| Approach: | They propose a model merging technique that capitalizes on similarities between low-rank adapters to reduce storage costs and improve performance. |
| Outcome: | The proposed method significantly reduces storage size (48% reduction) while outperforms existing merging techniques in terms of performance (0.2-1.8% drop). |