KS-Lottery: Finding Certified Lottery Tickets for Multilingual Language Models
<|reference_start|>KS-Lottery: Finding Certified Lottery Tickets for Multilingual Language Models: The lottery ticket hypothesis posits the existence of ``winning tickets'' within a randomly initialized neural network. Do winning tickets exist for LLMs in fine-tuning scenarios? How can we find such winning tickets? In this paper, we propose KS-Lottery, a method to identify a small subset of LLM parameters highly effective in multilingual fine-tuning. Our key idea is to use Kolmogorov-Smirnov Test to analyze the distribution shift of parameters before and after fine-tuning. We further theoretically prove that KS-Lottery can find the certified winning tickets in the embedding layer, fine-tuning on the found parameters is guaranteed to perform as well as full fine-tuning. Comparing KS-Lottery with other parameter-efficient tuning algorithms on translation tasks, the experimental results show that KS-Lottery finds a much smaller set of parameters for fine-tuning while achieving the comparable performance as full fine-tuning LLM. Surprisingly, we find that fine-tuning 18 tokens' embedding of LLaMA suffices to reach the fine-tuning translation performance~\footnote{https://github.com/CONE-MT/KS-Lottery.}.<|reference_end|>
@article{yuan2024ks-lottery:,
    title={KS-Lottery: Finding Certified Lottery Tickets for Multilingual Language
  Models},
    author={Fei Yuan, Chang Ma, Shuai Yuan, Qiushi Sun, Lei Li},
    journal={arXiv preprint arXiv:2402.02801},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02801},
    primaryClass={cs.CL cs.AI cs.LG}
}