Related papers: Towards Interpretable Soft Prompts

Towards Interpretable Soft Prompts

URL: http://arxiv.org/abs/2504.02144v1
Date: Wed, 02 Apr 2025 21:42:09 GMT
Title: Towards Interpretable Soft Prompts
Authors: Oam Patel, Jason Wang, Nikhil Shivakumar Nayak, Suraj Srinivas, Himabindu Lakkaraju,
Abstract summary: We evaluate the interpretability of trainable prompts based on two desiderata: faithfulness and scrutability.<n>Our experiments with GPT-2 demonstrate a fundamental trade-off between interpretability and the task-performance of the trainable prompt.
Score: 24.304585350085315
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Soft prompts have been popularized as a cheap and easy way to improve task-specific LLM performance beyond few-shot prompts. Despite their origin as an automated prompting method, however, soft prompts and other trainable prompts remain a black-box method with no immediately interpretable connections to prompting. We create a novel theoretical framework for evaluating the interpretability of trainable prompts based on two desiderata: faithfulness and scrutability. We find that existing methods do not naturally satisfy our proposed interpretability criterion. Instead, our framework inspires a new direction of trainable prompting methods that explicitly optimizes for interpretability. To this end, we formulate and test new interpretability-oriented objective functions for two state-of-the-art prompt tuners: Hard Prompts Made Easy (PEZ) and RLPrompt. Our experiments with GPT-2 demonstrate a fundamental trade-off between interpretability and the task-performance of the trainable prompt, explicating the hardness of the soft prompt interpretability problem and revealing odd behavior that arises when one optimizes for an interpretability proxy.

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