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Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2020.acl-main.386.pdf

Summary

This ACL 2020 position paper by Jacovi and Goldberg addresses a conceptual gap in NLP interpretability. The field has no consistent, formal notion of a faithful explanation, meaning one that accurately reflects how a model actually reached its prediction. The paper introduces no dataset or model. Instead, the authors survey existing evaluation practice and argue that faithfulness is routinely confused with plausibility, which is how convincing an explanation looks to people.

They make three contributions. First, they distill the community's implicit working definitions into three explicit assumptions. Second, they give practical guidelines for faithfulness evaluation. Third, they criticise the habit of treating faithfulness as a yes/no property. Any interpretation is a lossy approximation of a highly non-linear model, so counter-examples will almost always exist, and showing that a method is "not faithful" teaches little. The authors therefore propose graded faithfulness, assessed per model or task, or per region of the input space.

The work matters because it gives researchers a shared vocabulary. It also warns against evaluation setups that quietly measure plausibility rather than faithfulness.

Key points

  • The paper offers five guidelines for faithfulness evaluation:
    • State explicitly which property is being evaluated.
    • Avoid human judgement of explanation quality.
    • Avoid reliance on gold labels.
    • Treat "inherently interpretable" claims, such as attention, as unverified.
    • Do not use end-user task performance in IUI/HCI settings as a proxy for faithfulness.
  • Current approaches are organised around three assumptions. Two examples:
    • The Model Assumption links identical predictions to identical reasoning.
    • The Linearity Assumption holds that input parts carry independent importance, which is what justifies heat-map explanations.
  • Heat-maps can be stress-tested in several ways:
    • Erasure removes the most or least relevant tokens and checks whether the prediction changes.
    • The comprehensiveness and sufficiency metrics, drawn from work by DeYoung et al. (2019) and Yu et al. (2019), give this idea a formal footing.
  • A thought experiment shows the risk of utility-based evaluation. Explanations that are random yet look plausible only when the model is correct can still improve user performance.
  • Strictly faithful interpretation is called a likely "unicorn". The authors set graded faithfulness criteria as an open challenge for the community.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

267 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 258 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.acl-main.386

@inproceedings{jacovi2020faithfully,
  title = {{Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?}},
  author = {Jacovi, Alon and Goldberg, Yoav},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.386},
  url = {https://doi.org/10.18653/v1/2020.acl-main.386}
}

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