Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, Thomas Lukasiewicz, Jakob Grue Simonsen, Isabelle Augenstein
| Challenge: | Existing methods for explaining neural models are misleading as they often present reasons that are unfaithful to the model’s inner workings. |
| Approach: | They propose a counterfactual input editor for inserting reasons that lead to counterfacts but are not reflected by the NLEs. |
| Outcome: | The proposed model can evaluate emerging NLE models, proving a fundamental tool in the development of faithful explanations. |
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George Filandrianos, Edmund Dervakos, Orfeas Menis Mastromichalakis, Chrysoula Zerva, Giorgos Stamou
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Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are capable of generating persuasive Natural Language Explanations (NLEs) however, the faithfulness of these explanations should not be readily trusted at face value. |
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Self-Critique and Refinement for Faithful Natural Language Explanations (2025.emnlp-main)
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| Challenge: | Existing work has demonstrated that Large Language Models (LLMs) can self-critique and refine their initial outputs, but this capability remains unexplored for improving explanation faithfulness. |
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| Challenge: | In many applications of ML systems it is important to understand why the system came to a particular answer. |
| Approach: | They introduce a faithfulness metric based on counterfactual input edits that takes into account not just the binary label change, but the total shift in the model’s predicted label distribution. |
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A Causal Lens for Evaluating Faithfulness Metrics (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) offer natural language explanations as an alternative to feature attribution methods for model interpretability, but they may not reflect the model’s truereasoning faithfully. |
| Approach: | They propose a testbed framework for evaluating faithfulness metrics for natural language explanations using diagnosticity and model-editing methods. |
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| Challenge: | Prior work has focused on generating convincing rationales that appear to be subjectively faithful, but it remains unclear whether these explanations are epistemic faithful. |
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Evaluating Saliency Methods for Neural Language Models (2021.naacl-main)
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| Challenge: | a general complaint of neural network models is that their internal decision mechanisms are hard to understand. |
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NILE : Natural Language Inference with Faithful Natural Language Explanations (2020.acl-main)
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| Challenge: | Recent growth in popularity of deep learning models on NLP classification tasks has accompanied the need for generating some form of natural language explanation of predicted labels. |
| Approach: | They propose a novel method which generates labels along with its faithful explanations. |
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Are self-explanations from Large Language Models faithful? (2024.findings-acl)
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| Challenge: | Instruction-tuned Large Language Models excel at many tasks and will explain their reasoning, so-called self-explanations. |
| Approach: | They propose to employ self-consistency checks to measure faithfulness to LLMs to determine if they are model-dependent and if their reasoning is convincing and wrong. |
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LLMs Don’t Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations (2025.emnlp-main)
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Harry Mayne, Ryan Othniel Kearns, Yushi Yang, Andrew M. Bean, Eoin D. Delaney, Chris Russell, Adam Mahdi
| Challenge: | Existing studies on language models' ability to explain their decisions in natural language have focused on self-generated counterfactual explanations (SCEs). |
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