Graph-Guided Textual Explanation Generation Framework

• URL: http://arxiv.org/abs/2412.12318v2
• Date: Thu, 20 Feb 2025 07:08:17 GMT
• Title: Graph-Guided Textual Explanation Generation Framework
• Authors: Shuzhou Yuan, Jingyi Sun, Ran Zhang, Michael Färber, Steffen Eger, Pepa Atanasova, Isabelle Augenstein,
• Abstract summary: Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model's reasoning about its predictions. We propose G-Tex, a Graph-Guided Textual Explanation Generation framework designed to enhance the faithfulness of NLEs.
• Score: 57.2027753204786
• License:
• 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 T5 and BART using three reasoning datasets show 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.

Related papers

• Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks [12.892400744247565]
  We develop a novel interpretable causal GNN framework that incorporates retrieval-based causal learning with Graph Information Bottleneck (GIB) theory. We achieve 32.71% higher precision on real-world explanation scenarios with diverse explanation types.
  arXiv  Detail & Related papers  (2024-02-07T09:57:39Z)
• Situated Natural Language Explanations [54.083715161895036]
  Natural language explanations (NLEs) are among the most accessible tools for explaining decisions to humans. Existing NLE research perspectives do not take the audience into account. Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations.
  arXiv  Detail & Related papers  (2023-08-27T14:14:28Z)
• Faithfulness Tests for Natural Language Explanations [87.01093277918599]
  Explanations of neural models aim to reveal a model's decision-making process for its predictions. Recent work shows that current methods giving explanations such as saliency maps or counterfactuals can be misleading. This work explores the challenging question of evaluating the faithfulness of natural language explanations.
  arXiv  Detail & Related papers  (2023-05-29T11:40:37Z)
• Benchmarking Faithfulness: Towards Accurate Natural Language Explanations in Vision-Language Tasks [0.0]
  Natural language explanations (NLEs) promise to enable the communication of a model's decision-making in an easily intelligible way. While current models successfully generate convincing explanations, it is an open question how well the NLEs actually represent the reasoning process of the models. We propose three faithfulness metrics: Attribution-Similarity, NLE-Sufficiency, and NLE-Comprehensiveness.
  arXiv  Detail & Related papers  (2023-04-03T08:24:10Z)
• The Unreliability of Explanations in Few-Shot In-Context Learning [50.77996380021221]
  We focus on two NLP tasks that involve reasoning over text, namely question answering and natural language inference. We show that explanations judged as good by humans--those that are logically consistent with the input--usually indicate more accurate predictions. We present a framework for calibrating model predictions based on the reliability of the explanations.
  arXiv  Detail & Related papers  (2022-05-06T17:57:58Z)
• SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods [0.0]
  We propose a method to improve the explanation quality of node classification tasks through aggregation of auxiliary explanations. Applying SEEN does not require modification of a graph and can be used with diverse explainability techniques. Experiments on matching motif-participating nodes from a given graph show great improvement in explanation accuracy of up to 12.71%.
  arXiv  Detail & Related papers  (2021-06-16T03:04:46Z)
• ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning [65.15423587105472]
  We present a new generative and structured commonsense-reasoning task (and an associated dataset) of explanation graph generation for stance prediction. Specifically, given a belief and an argument, a model has to predict whether the argument supports or counters the belief and also generate a commonsense-augmented graph that serves as non-trivial, complete, and unambiguous explanation for the predicted stance. A significant 83% of our graphs contain external commonsense nodes with diverse structures and reasoning depths.
  arXiv  Detail & Related papers  (2021-04-15T17:51:36Z)
• LIREx: Augmenting Language Inference with Relevant Explanation [1.4780878458667916]
  Natural language explanations (NLEs) are a form of data annotation in which annotators identify rationales when assigning labels to data instances. NLEs have been shown to capture human reasoning better, but not as beneficial for natural language inference. We propose a novel framework, LIREx, that incorporates both a rationale-enabled explanation generator and an instance selector to select only relevant NLEs.
  arXiv  Detail & Related papers  (2020-12-16T18:49:29Z)
• Parameterized Explainer for Graph Neural Network [49.79917262156429]
  We propose PGExplainer, a parameterized explainer for Graph Neural Networks (GNNs) Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting easily. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7% relative improvement in AUC on explaining graph classification.
  arXiv  Detail & Related papers  (2020-11-09T17:15:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.