On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model

• URL: http://arxiv.org/abs/2311.07820v1
• Date: Tue, 14 Nov 2023 00:43:33 GMT
• Title: On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model
• Authors: Nohil Park, Joonsuk Park, Kang Min Yoo, Sungroh Yoon
• Abstract summary: We study the interaction between parameter-efficient fine-tuning (PEFT) and cross-lingual tasks in multilingual autoregressive models. We show that prompt tuning is more effective in enhancing the performance of low-resource languages than fine-tuning.
• Score: 49.81429697921861
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An exciting advancement in the field of multilingual models is the emergence of autoregressive models with zero- and few-shot capabilities, a phenomenon widely reported in large-scale language models. To further improve model adaptation to cross-lingual tasks, another trend is to further fine-tune the language models with either full fine-tuning or parameter-efficient tuning. However, the interaction between parameter-efficient fine-tuning (PEFT) and cross-lingual tasks in multilingual autoregressive models has yet to be studied. Specifically, we lack an understanding of the role of linguistic distributions in multilingual models in the effectiveness of token-based prompt tuning. To address this question, we conduct experiments comparing prompt tuning and fine-tuning on the decoder-based multilingual model, XGLM, with four cross-lingual tasks (XNLI, PAWS-X, POS, NER). According to our study, prompt tuning achieves on par or better performance over fine-tuning across all languages while updating at most 0.13\% of the model parameters. Moreover, we empirically show that prompt tuning is more effective in enhancing the performance of low-resource languages than fine-tuning. Our further analysis shows that the phenomenon is related to the tokenization scheme of the multilingual model.

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