Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models
<|reference_start|>Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models: 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.<|reference_end|>
@article{kamahi2024counterfactuals,
    title={Counterfactuals As a Means for Evaluating Faithfulness of Attribution
  Methods in Autoregressive Language Models},
    author={Sepehr Kamahi, Yadollah Yaghoobzadeh},
    journal={arXiv preprint arXiv:2408.11252},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11252},
    primaryClass={cs.CL}
}