A Necessary Step toward Faithfulness: Measuring and Improving Consistency in Free-Text Explanations (2025.emnlp-main)
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| Challenge: | a measure of faithful free-text explanations is difficult to generate by language models and assess by humans. |
| Approach: | They propose a measure of Prediction-EXplanation consistency by extending the concept of weight of evidence. |
| Outcome: | The proposed measure improves explanation faithfulness by up to 9.7%, the authors show . they show that applying preference optimization improves the consistency of generated explanations across three model families. |
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| Challenge: | Instruction-tuned Large Language Models excel at many tasks and will explain their reasoning, so-called self-explanations. |
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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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| Challenge: | Recent work in Natural Language Processing has focused on extracting faithful explanations . yet, little is known about how post-hoc explanations perform in out-of-domain settings . |
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| Challenge: | Large language models (LLMs) are often prompted to produce natural language explanations, but the features driving the answer are often different from those emphasized in their explanations. |
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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. |
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