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        <title>LM-Cocktail: Resilient Tuning of Language Models via Model Merging</title>
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        <namePart type="given">Shitao</namePart>
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    <abstract>The pre-trained language models are continually fine-tuned to better support downstream applications. However, this operation may result in significant performance degeneration on general tasks beyond the targeted domain. To overcome this problem, we propose LM-Cocktail which enables the fine-tuned model to stay resilient in general perspectives. Our method is conducted in the form of model merging, where the fine-tuned language model is merged with the pre-trained base model or the peer models from other domains through weighted average. Despite simplicity, LM-Cocktail is surprisingly effective: the resulted model is able to achieve a strong empirical performance in the whole scope of general tasks while preserving a superior capacity in its targeted domain.</abstract>
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%0 Conference Proceedings
%T LM-Cocktail: Resilient Tuning of Language Models via Model Merging
%A Xiao, Shitao
%A Liu, Zheng
%A Zhang, Peitian
%A Xing, Xingrun
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F xiao-etal-2024-lm
%X The pre-trained language models are continually fine-tuned to better support downstream applications. However, this operation may result in significant performance degeneration on general tasks beyond the targeted domain. To overcome this problem, we propose LM-Cocktail which enables the fine-tuned model to stay resilient in general perspectives. Our method is conducted in the form of model merging, where the fine-tuned language model is merged with the pre-trained base model or the peer models from other domains through weighted average. Despite simplicity, LM-Cocktail is surprisingly effective: the resulted model is able to achieve a strong empirical performance in the whole scope of general tasks while preserving a superior capacity in its targeted domain.
%R 10.18653/v1/2024.findings-acl.145
%U https://aclanthology.org/2024.findings-acl.145/
%U https://doi.org/10.18653/v1/2024.findings-acl.145
%P 2474-2488

Markdown (Informal)

[LM-Cocktail: Resilient Tuning of Language Models via Model Merging](https://aclanthology.org/2024.findings-acl.145/) (Xiao et al., Findings 2024)

• LM-Cocktail: Resilient Tuning of Language Models via Model Merging (Xiao et al., Findings 2024)

ACL

• Shitao Xiao, Zheng Liu, Peitian Zhang, and Xingrun Xing. 2024. LM-Cocktail: Resilient Tuning of Language Models via Model Merging. In Findings of the Association for Computational Linguistics: ACL 2024, pages 2474–2488, Bangkok, Thailand. Association for Computational Linguistics.