MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension

• URL: http://arxiv.org/abs/2310.18167v1
• Date: Fri, 27 Oct 2023 14:24:06 GMT
• Title: MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
• Authors: Guoxin Chen and Yiming Qian and Bowen Wang and Liangzhi Li
• Abstract summary: We propose a multi-level prompt tuning (MPrompt) method for machine reading comprehension. It utilizes prompts at task-specific, domain-specific, and context-specific levels to enhance the comprehension of input semantics. We conducted extensive experiments on 12 benchmarks of various QA formats and achieved an average improvement of 1.94% over the state-of-the-art methods.
• Score: 19.12663587559988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The large language models have achieved superior performance on various natural language tasks. One major drawback of such approaches is they are resource-intensive in fine-tuning new datasets. Soft-prompt tuning presents a resource-efficient solution to fine-tune the pre-trained language models (PLMs) while keeping their weight frozen. Existing soft prompt methods mainly focus on designing the input-independent prompts that steer the model to fit the domain of the new dataset. Those methods often ignore the fine-grained information about the task and context of the text. In this paper, we propose a multi-level prompt tuning (MPrompt) method for machine reading comprehension. It utilizes prompts at task-specific, domain-specific, and context-specific levels to enhance the comprehension of input semantics at different granularities. We also propose an independence constraint to steer each domain-specific prompt to focus on information within its domain to avoid redundancy. Moreover, we present a prompt generator that incorporates context-related knowledge in the prompt generation to enhance contextual relevancy. We conducted extensive experiments on 12 benchmarks of various QA formats and achieved an average improvement of 1.94\% over the state-of-the-art methods.

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