HeteroFusion fuses heterogeneous LLMs via topology-based alignment and conflict-aware denoising, outperforming merging and ensemble baselines in cross-family and multi-source settings.
Model stock: All we need is just a few fine-tuned models
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The paper introduces a new taxonomy for model merging methods and reviews their applications in LLMs, MLLMs, continual learning, multi-task learning, and other subfields while outlining open challenges.
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Can Heterogeneous Language Models Be Fused?
HeteroFusion fuses heterogeneous LLMs via topology-based alignment and conflict-aware denoising, outperforming merging and ensemble baselines in cross-family and multi-source settings.
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Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
The paper introduces a new taxonomy for model merging methods and reviews their applications in LLMs, MLLMs, continual learning, multi-task learning, and other subfields while outlining open challenges.