Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? (2020.findings-emnlp)
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| Challenge: | Existing models that generate NL explanations for tasks have been evaluated on the basis of surface-level similarities to human explanations, both through automatic metrics like BLEU and human evaluations. |
| Approach: | They propose to use a model as a proxy for a human observer to evaluate NL explanations from the model simulatability perspective. |
| Outcome: | The proposed model-generated explanations are evaluated on the basis of surface-level similarities to human explanations, both through automatic metrics like BLEU and human evaluations. |
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| Challenge: | Existing evaluation metrics focus only on the quality of the induced space of possible concepts, neglecting the latter. |
| Approach: | They propose to use large language models as simulators to approximate the evaluation and report various analyses to make such approximations reliable. |
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Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations (2023.acl-long)
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| Challenge: | Human-annotated labels and explanations are critical for training explainable NLP models. |
| Approach: | They propose a metric that measures the usefulness of an explanation for model performance at both fine-tuning and inference. |
| Outcome: | The proposed metric can evaluate the quality of human-annotated explanations, while Simulatability falls short. |
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? (2020.acl-main)
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| Challenge: | a new study examines the impact of algorithmic explanations on simulatability of machine learning models . a model is simulatable when a person can predict its behavior on new inputs . |
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A Study of Automatic Metrics for the Evaluation of Natural Language Explanations (2021.eacl-main)
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| Challenge: | a lack of transparency is a key issue for robotics and AI. |
| Approach: | They propose to map existing automatic evaluation methods for natural language generation onto explanations. |
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On Evaluating Explanation Utility for Human-AI Decision Making in NLP (2024.findings-emnlp)
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| Challenge: | a lack of evidence that explanations help people in situations they are introduced for is a problem in NLP . prior work on explainability has focused on overcoming technical challenges and used proxy evaluations. |
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On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation (2021.acl-long)
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| Challenge: | Existing methods for explaining "black-box" models such as Influence Functions are becoming more popular. |
| Approach: | They propose a semantic-based evaluation metric that can better align with humans’ judgment of explanations than the widely adopted diagnostic or re-training measures. |
| Outcome: | The proposed method can better align with humans’ judgment of explanations than diagnostic or re-training measures. |
Reframing Human-AI Collaboration for Generating Free-Text Explanations (2022.naacl-main)
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| Challenge: | Large language models are capable of generating fluent-appearing text with little task-specific supervision. |
| Approach: | They propose a pipeline that combines GPT-3 with a supervised filter that incorporates binary acceptability judgments from humans in the loop. |
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Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models (2025.naacl-long)
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| Challenge: | In the recent past, a popular way of evaluating natural language understanding was to consider a model’s ability to perform natural language inference (NLI) tasks. |
| Approach: | They focus on five different NLI benchmarks across six models of different scales and examine how their accuracies develop during training. |
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A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
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| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
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Explain Yourself! Leveraging Language Models for Commonsense Reasoning (P19-1)
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| Challenge: | Empirical results indicate that we can effectively leverage language models for commonsense reasoning. |
| Approach: | They propose to use commonsense auto-generated explanations to train language models to generate explanations that can be used during training and inference in a commonsensense Auto-Generated Explanation framework. |
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