@inproceedings{zhao-iii-2025-necessary,
    title = "A Necessary Step toward Faithfulness: Measuring and Improving Consistency in Free-Text Explanations",
    author = "Zhao, Lingjun  and
      Iii, Hal Daum{\'e}",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.797/",
    pages = "15810--15824",
    ISBN = "979-8-89176-332-6",
    abstract = "Faithful free-text explanations are important to ensure transparency in high-stakes AI decision-making contexts, but they are challenging to generate by language models and assess by humans. In this paper, we present a measure for Prediction-EXplanation (PEX) consistency, by extending the concept of weight of evidence. This measure quantifies how much a free-text explanation supports or opposes a prediction, serving as an important aspect of explanation faithfulness. Our analysis reveals that more than 62{\%} explanations generated by large language models lack this consistency. We show that applying direct preference optimization improves the consistency of generated explanations across three model families, with improvement ranging from 43.1{\%} to 292.3{\%}. Furthermore, we demonstrate that optimizing this consistency measure can improve explanation faithfulness by up to 9.7{\%}."
}