Effectively Prompting Small-sized Language Models for Cross-lingual Tasks via Winning Tickets

• URL: http://arxiv.org/abs/2404.01242v1
• Date: Mon, 1 Apr 2024 17:03:16 GMT
• Title: Effectively Prompting Small-sized Language Models for Cross-lingual Tasks via Winning Tickets
• Authors: Mingqi Li, Feng Luo,
• Abstract summary: Current soft prompt methods yield limited performance when applied to small-sized models. Deep prompt-tuning entails prepending parameters in each prompt for enhanced efficacy. We introduce the Lottery Ticket Prompt-learning framework that integrates winning tickets with soft prompts.
• Score: 2.803947848713182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current soft prompt methods yield limited performance when applied to small-sized models (fewer than a billion parameters). Deep prompt-tuning, which entails prepending parameters in each layer for enhanced efficacy, presents a solution for prompting small-sized models, albeit requiring carefully designed implementation. In this paper, we introduce the Lottery Ticket Prompt-learning (LTP) framework that integrates winning tickets with soft prompts. The LTP offers a simpler implementation and requires only a one-time execution. We demonstrate LTP on cross-lingual tasks, where prior works rely on external tools like human-designed multilingual templates and bilingual dictionaries, which may not be feasible in a low-resource regime. Specifically, we select a subset of parameters that have been changed the most during the fine-tuning with the Masked Language Modeling objective. Then, we prepend soft prompts to the original pre-trained language model and only update the selected parameters together with prompt-related parameters when adapting to the downstream tasks. We verify the effectiveness of our LTP framework on cross-lingual tasks, specifically targeting low-resource languages. Our approach outperforms the baselines by only updating 20\% of the original parameters.

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