Abstract
Prompt tuning learns soft prompts to condition the frozen Pre-trained Language Models (PLMs) for performing downstream tasks in a parameter-efficient manner. While prompt tuning has gradually reached the performance level of fine-tuning as the model scale increases, there is still a large performance gap between prompt tuning and fine-tuning for models of moderate and small scales (typically less than 11B parameters). In this paper, we empirically show that the trained prompt tokens can have a negative impact on a downstream task and thus degrade its performance. To bridge the gap, we propose a novel PROMPT tuning model with an eXtremely small scale (XPROMPT) under the regime of lottery tickets hypothesis. Specifically, XPROMPT eliminates the negative prompt tokens at different granularity levels through a hierarchical structured pruning, yielding a more parameter-efficient prompt yet with a competitive performance. Comprehensive experiments are carried out on the SuperGLUE tasks, and the results indicate that XPROMPT is able to close the performance gap at smaller model scales.
Original language | English |
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Pages | 11033-11047 |
Number of pages | 15 |
Publication status | Published - 2022 |
Event | 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 - Abu Dhabi, United Arab Emirates Duration: 7 Dec 2022 → 11 Dec 2022 |
Conference
Conference | 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 |
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Country/Territory | United Arab Emirates |
City | Abu Dhabi |
Period | 7/12/22 → 11/12/22 |