@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://aclanthology.org/2025.emnlp-main.1494/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T Graph-Guided Textual Explanation Generation Framework
%A Yuan, Shuzhou
%A Sun, Jingyi
%A Zhang, Ran
%A Färber, Michael
%A Eger, Steffen
%A Atanasova, Pepa
%A Augenstein, Isabelle
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F yuan-etal-2025-graph
%X 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.
%R 10.18653/v1/2025.emnlp-main.1494
%U https://aclanthology.org/2025.emnlp-main.1494/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1494
%P 29350-29374
Markdown (Informal)
[Graph-Guided Textual Explanation Generation Framework](https://aclanthology.org/2025.emnlp-main.1494/) (Yuan et al., EMNLP 2025)
ACL
- Shuzhou Yuan, Jingyi Sun, Ran Zhang, Michael Färber, Steffen Eger, Pepa Atanasova, and Isabelle Augenstein. 2025. Graph-Guided Textual Explanation Generation Framework. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 29350–29374, Suzhou, China. Association for Computational Linguistics.