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        <title>PromptExplainer: Explaining Language Models through Prompt-based Learning</title>
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    <abstract>Pretrained language models have become workhorses for various natural language processing (NLP) tasks, sparking a growing demand for enhanced interpretability and transparency. However, prevailing explanation methods, such as attention-based and gradient-based strategies, largely rely on linear approximations, potentially causing inaccuracies such as accentuating irrelevant input tokens. To mitigate the issue, we develop PromptExplainer, a novel method for explaining language models through prompt-based learning. PromptExplainer aligns the explanation process with the masked language modeling (MLM) task of pretrained language models and leverages the prompt-based learning framework for explanation generation. It disentangles token representations into the explainable embedding space using the MLM head and extracts discriminative features with a verbalizer to generate class-dependent explanations. Extensive experiments demonstrate that PromptExplainer significantly outperforms state-of-the-art explanation methods.</abstract>
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%0 Conference Proceedings
%T PromptExplainer: Explaining Language Models through Prompt-based Learning
%A Feng, Zijian
%A Zhou, Hanzhang
%A Zhu, Zixiao
%A Mao, Kezhi
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F feng-etal-2024-promptexplainer
%X Pretrained language models have become workhorses for various natural language processing (NLP) tasks, sparking a growing demand for enhanced interpretability and transparency. However, prevailing explanation methods, such as attention-based and gradient-based strategies, largely rely on linear approximations, potentially causing inaccuracies such as accentuating irrelevant input tokens. To mitigate the issue, we develop PromptExplainer, a novel method for explaining language models through prompt-based learning. PromptExplainer aligns the explanation process with the masked language modeling (MLM) task of pretrained language models and leverages the prompt-based learning framework for explanation generation. It disentangles token representations into the explainable embedding space using the MLM head and extracts discriminative features with a verbalizer to generate class-dependent explanations. Extensive experiments demonstrate that PromptExplainer significantly outperforms state-of-the-art explanation methods.
%U https://aclanthology.org/2024.findings-eacl.60
%P 882-895

Markdown (Informal)

[PromptExplainer: Explaining Language Models through Prompt-based Learning](https://aclanthology.org/2024.findings-eacl.60) (Feng et al., Findings 2024)

• PromptExplainer: Explaining Language Models through Prompt-based Learning (Feng et al., Findings 2024)

ACL

• Zijian Feng, Hanzhang Zhou, Zixiao Zhu, and Kezhi Mao. 2024. PromptExplainer: Explaining Language Models through Prompt-based Learning. In Findings of the Association for Computational Linguistics: EACL 2024, pages 882–895, St. Julian’s, Malta. Association for Computational Linguistics.