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The large language models have achieved superior performance on variousnatural language tasks. One major drawback of such approaches is they areresource-intensive in fine-tuning new datasets. Soft-prompt tuning presents aresource-efficient solution to fine-tune the pre-trained language models (PLMs)while keeping their weight frozen. Existing soft prompt methods mainly focus ondesigning the input-independent prompts that steer the model to fit the domainof the new dataset. Those methods often ignore the fine-grained informationabout the task and context of the text. In this paper, we propose a multi-levelprompt tuning (MPrompt) method for machine reading comprehension. It utilizesprompts at task-specific, domain-specific, and context-specific levels toenhance the comprehension of input semantics at different granularities. Wealso propose an independence constraint to steer each domain-specific prompt tofocus on information within its domain to avoid redundancy. Moreover, wepresent a prompt generator that incorporates context-related knowledge in theprompt generation to enhance contextual relevancy. We conducted extensiveexperiments on 12 benchmarks of various QA formats and achieved an averageimprovement of 1.94\% over the state-of-the-art methods.

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