LM-Cocktail: Resilient Tuning of Language Models via Model Merging
Shitao Xiao, Zheng Liu, Peitian Zhang, Xingrun Xing
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.We conduct comprehensive experiments with LLama and BGE models on popular benchmarks, including FLAN, MMLU, MTEB, whose results validate the efficacy of our proposed method.