@inproceedings{yuan-etal-2025-graph,
    title = "Graph-Guided Textual Explanation Generation Framework",
    author = {Yuan, Shuzhou  and
      Sun, Jingyi  and
      Zhang, Ran  and
      F{\"a}rber, Michael  and
      Eger, Steffen  and
      Atanasova, Pepa  and
      Augenstein, Isabelle},
    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.1494/",
    pages = "29350--29374",
    ISBN = "979-8-89176-332-6",
    abstract = "Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model{'}s reasoning about its predictions. However, recent work has questioned their faithfulness, as they may not accurately reflect the model{'}s internal reasoning process regarding its predicted answer. In contrast, highlight explanations{--}input fragments critical for the model{'}s predicted answers{--}exhibit measurable faithfulness. Building on this foundation, we propose G-TEx, a Graph-Guided Textual Explanation Generation framework designed to enhance the faithfulness of NLEs. Specifically, highlight explanations are first extracted as faithful cues reflecting the model{'}s reasoning logic toward answer prediction. They are subsequently encoded through a graph neural network layer to guide the NLE generation, which aligns the generated explanations with the model{'}s underlying reasoning toward the predicted answer. Experiments on both encoder-decoder and decoder-only models across three reasoning datasets demonstrate that G-TEx improves NLE faithfulness by up to 12.18{\%} compared to baseline methods. Additionally, G-TEx generates NLEs with greater semantic and lexical similarity to human-written ones. Human evaluations show that G-TEx can decrease redundant content and enhance the overall quality of NLEs. Our work presents a novel method for explicitly guiding NLE generation to enhance faithfulness, serving as a foundation for addressing broader criteria in NLE and generated text."
}