Evaluating Input Feature Explanations through a Unified Diagnostic Evaluation Framework (2025.naacl-long)
Copied to clipboard
| Challenge: | Input feature explanations reveal how a model makes decisions based on a specific input. |
| Approach: | They propose a framework that facilitates an automated comparison between highlight and interactive explanations comprised of four diagnostic properties. |
| Outcome: | The proposed framework compares highlight and interactive explanations across two datasets and two models and shows that interactive span explanations outperform other explanation types across most diagnostic properties. |
Similar Papers
SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)
Copied to clipboard
| Challenge: | Existing models with opacity problems have been proposed to address this problem. |
| Approach: | They propose a unified local-interpretability framework with a rigorous theoretical foundation on the game-theoretic concept of Shapley values. |
| Outcome: | The proposed framework is based on the Shapley-value-based model explanations. |
Human-Centered Evaluation of Explanations (2022.naacl-tutorials)
Copied to clipboard
Jordan Boyd-Graber, Samuel Carton, Shi Feng, Q. Vera Liao, Tania Lombrozo, Alison Smith-Renner, Chenhao Tan
| Challenge: | This tutorial will provide an overview of human-centered evaluations of explanations . |
| Approach: | This tutorial will provide an overview of human-centered evaluations of explanations . it will introduce the psychological foundation of explanation and types of NLP explanations. |
| Outcome: | This tutorial will provide an overview of human-centered evaluations of explanations . it will cover the two categories of evaluation: evaluation based on human-annotated explanations and evaluation with human-subjects studies. |
Explaining Interactions Between Text Spans (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing highlight-based explanations focus on identifying individual important features or interactions only between adjacent tokens or tuples of tokens. |
| Approach: | They propose a multi-annotator dataset of human span interaction explanations for NLU and FC. |
| Outcome: | The proposed method compares human reasoning processes to those of a fine-tuned large language model. |
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for generating explanations for neural networks ignore feature interactions between words and phrases. |
| Approach: | They propose to build hierarchical explanations by detecting feature interactions by combining words and phrases at different levels of the hierarchy. |
| Outcome: | The proposed method is evaluated on two benchmark datasets, via automatic and human evaluations. |
QED: A Framework and Dataset for Explanations in Question Answering (2021.tacl-1)
Copied to clipboard
Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, Michael Collins
| Challenge: | Existing question answering systems provide no explanation of reasoning that leads to answer . linguistically informed, extensible framework provides explanations in question answering . |
| Approach: | They propose a linguistically informed, extensible framework for explanations in question answering . they propose an expert-annotated dataset of QED explanations built upon a subset of the Natural Questions dataset . |
| Outcome: | The proposed framework improves the ability of untrained raters to spot errors in QA datasets. |
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. |
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models (2023.acl-demo)
Copied to clipboard
Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Existing models are susceptible to learning spurious biases that do not reflect the underlying task. |
| Approach: | They propose an open-source framework for explanation-based model debugging that allows users to provide various forms of feedback on model explanations. |
| Outcome: | The proposed framework improves model’s OOD performance on text classification tasks by up to 18%. |
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models and evaluation settings have shortcomings regarding the coupling of answer and explanation which might cause serious issues in user experience. |
| Approach: | They propose a hierarchical model and a new regularization term to strengthen the coupling of answer and explanation and two evaluation scores to quantify the couple. |
| Outcome: | The proposed model strengthens the answer-explanation coupling and provides evaluation scores that align with user experience. |
Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations (2021.findings-acl)
Copied to clipboard
| Challenge: | despite interest in explainable AI, there is increasing skepticism as to whether explanations are useful to end-users in downstream applications. |
| Approach: | They conduct user studies to measure whether explanations help users decide when to accept or reject an ODQA system's answer. |
| Outcome: | The proposed study shows that explanations outperform baselines across modalities but the best strategy varies with the modality. |