ORBIT preserves foundational language capabilities during generative retrieval fine-tuning by using origin-regulated weight averaging to constrain parameter drift beyond a distance threshold.
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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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ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging
ORBIT preserves foundational language capabilities during generative retrieval fine-tuning by using origin-regulated weight averaging to constrain parameter drift beyond a distance threshold.
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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.