Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models
Sepehr Kamahi, Yadollah Yaghoobzadeh · 2024
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-ofdistribution 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, indistribution counterfactuals, making the evaluation protocol more reliable.