Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models

• URL: http://arxiv.org/abs/2408.11252v3
• Date: Wed, 9 Oct 2024 17:12:50 GMT
• Title: Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models
• Authors: Sepehr Kamahi, Yadollah Yaghoobzadeh,
• Abstract summary: We propose a technique that leverages counterfactual generation to evaluate the faithfulness of attribution methods for autoregressive language models. Our technique generates fluent, in-distribution counterfactuals, making the evaluation protocol more reliable.
• Score: 6.394084132117747
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the widespread adoption of autoregressive language models, explainability evaluation research has predominantly focused on span infilling and masked language models. Evaluating the faithfulness of an explanation method -- how accurately it explains the inner workings and decision-making of the model -- is challenging because it is difficult to separate the model from its explanation. Most faithfulness evaluation techniques corrupt or remove input tokens deemed important by a particular attribution (feature importance) method and observe the resulting change in the model's output. However, for autoregressive language models, this approach creates out-of-distribution inputs due to their next-token prediction training objective. In this study, we propose a technique that leverages counterfactual generation to evaluate the faithfulness of attribution methods for autoregressive language models. Our technique generates fluent, in-distribution counterfactuals, making the evaluation protocol more reliable.

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