| Challenge: | Feature attribution methods are often evaluated on metrics such as comprehensiveness and sufficiency. |
| Approach: | They propose to use beam search to define problem of optimizing an explanation for a metric . they also propose to evaluate the metric on one or more metrics to determine its solvability . |
| Outcome: | The proposed explainer can solve the problem of optimizing an explanation for a metric by beam search. |
Similar Papers
Goodhart’s Law Applies to NLP’s Explanation Benchmarks (2024.findings-eacl)
Copied to clipboard
| Challenge: | Popular methods for "explaining" the outputs of natural language processing (NLP) models operate by highlighting a subset of input tokens that ought, in some sense, to be salient. |
| Approach: | They propose to inflate a model’s comprehensiveness and sufficiency scores dramatically without altering its predictions or explanations on in-distribution inputs. |
| Outcome: | The proposed metrics exploit the tendency for extracted explanations and complements to be “out-of-support” relative to each other and in-distribution inputs. |
Evaluating Explanation Methods for Neural Machine Translation (2020.acl-main)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) has seen great success during recent years. |
| Approach: | They propose a metric that measures the fidelity of explanation methods on translation tasks . they use an efficient approximation to evaluate several explanation methods . |
| Outcome: | The proposed metric is efficient and can be used on translation tasks. |
On Evaluating Explanation Utility for Human-AI Decision Making in NLP (2024.findings-emnlp)
Copied to clipboard
| 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. |
| Approach: | They propose to use existing metrics to evaluate the effectiveness of explanations in NLP . they argue that providing AI predictions does not cause decision makers to speed up work . |
| Outcome: | The proposed evaluations show that providing AI predictions does not cause decision makers to speed up their work without compromising performance. |
A Comparative Study of Faithfulness Metrics for Model Interpretability Methods (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to reveal the reasoning processes of machine learning models are difficult to interpret due to their complexity. |
| Approach: | They propose to use diagnosticity and complexity to assess faithfulness of machine learning models . they propose to apply posthoc interpretation methods to reveal reasoning behind models based on internal reasoning . |
| Outcome: | The proposed interpretation metrics show conflicting preferences when comparing interpretations . sufficiency and comprehensiveness metrics have higher diagnosticity and lower complexity . |
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation (2021.acl-long)
Copied to clipboard
| 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. |
EXPERT: An Explainable Image Captioning Evaluation Metric with Structured Explanations (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on explainable evaluation metrics generate explanations without standardized criteria and the overall quality of the generated explanations remains unverified. |
| Approach: | They propose a reference-free evaluation metric that provides structured explanations based on fluency, relevance, and descriptiveness. |
| Outcome: | The proposed evaluation template achieves state-of-the-art on benchmark datasets while providing significantly higher-quality explanations than existing metrics. |
The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics (2023.acl-short)
Copied to clipboard
| Challenge: | Neural metrics for machine translation evaluation are considered "black boxes" lexical overlap-based metrics are popular for evaluation of translation systems and algorithms . |
| Approach: | They develop and compare several neural explainability methods to understand translation errors . they aim to better understand the correspondence between token-level explanations and human annotated error spans . |
| Outcome: | The proposed methods leverage token-level information that can be directly attributed to translation errors. |
Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation (2023.emnlp-main)
Copied to clipboard
Yixin Liu, Alexander Fabbri, Yilun Zhao, Pengfei Liu, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev
| Challenge: | Compared to neural systems, automatic metrics should be interpretable and provide intuitive insights into system performance and output quality. |
| Approach: | They propose to use a two-stage evaluation pipeline to extract basic information units from one text sequence and check the extracted units in another sequence. |
| Outcome: | The proposed metrics can provide high interpretability at both the fine-grained unit level and summary level, and one-stage metrics that achieve a balance between efficiency and interpretability. |
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? (2020.acl-main)
Copied to clipboard
| 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 . |
| Approach: | They conduct human subject tests to isolate effect of algorithmic explanations on simulatability . they find ratings of explanations are not predictive of how helpful they are . |
| Outcome: | The results provide the first reliable estimates of how explanations influence simulatability . they show that ratings are not predictive of how helpful explanations are . |
A Diagnostic Study of Explainability Techniques for Text Classification (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing explainability techniques that can be produced post-hoc with already trained models are lacking a definitive guide on how to choose one given a particular task and model architecture. |
| Approach: | They propose to use a list of diagnostic properties to evaluate existing explainability techniques to compare them with human annotations of salient input regions. |
| Outcome: | The proposed list compares a set of explainability techniques on downstream text classification tasks and neural network architectures. |