Let the CAT out of the bag: Contrastive Attributed explanations for Text (2022.emnlp-main)
Copied to clipboard
| Challenge: | XAI has seen an explosion of interest in explaining black box behavior . contrastive/counterfactual explanations have seen a surge of interest recently . |
| Approach: | They propose a method which provides contrastive explanations for natural language text data with a novel twist by exploiting attribute classifiers. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four benchmark metrics. |
Similar Papers
Contrastive Explanations of Text Classifiers as a Service (2022.naacl-demo)
Copied to clipboard
| Challenge: | ContrXT provides time contrastive explanations of black box text classifiers by manipulating binary decision diagrams. |
| Approach: | They propose a system that provides time contrastive explanations of black box classifiers as a service by manipulating binary decision diagrams. |
| Outcome: | The proposed system has a throughput of 2.55 users per second and is available as a python pip package. |
Interpreting Language Models with Contrastive Explanations (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing explanation methods conflate evidence for various features to predict a token . existing explanation methods are less interpretable for human understanding . |
| Approach: | They propose to explain language models contrastively by looking for salient input tokens that explain why the model predicted one token instead of another. |
| Outcome: | The proposed explanations are better than non-contrastive explanations for language models . they show that contrastive explanations improve simulability for human observers . |
Contrastive Explanations for Model Interpretability (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for producing model explanations seek all causal factors at once, making them difficult to comprehend. |
| Approach: | They propose a method to produce contrastive explanations in the latent space . they use attribution and token/span attribution to produce models that consider only contrastive reasoning . |
| Outcome: | The proposed method allows model behavior to consider only contrastive reasoning . it also uncovers which aspects of the input are useful for and against particular decisions . |
KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches in NLP focus on “WHY A” rather than contrastive “WHA NOT B” Experimental results show that contrastive explanations are beneficial to fit the scenarios by clarifying the difference between the predicted answer and other possible wrong ones. |
| Approach: | They propose to generate contrastive explanations with counterfactual examples in NLI by identifying key phrases from input sentences and using them as key perturbations to generate counterfacts. |
| Outcome: | The proposed framework improves on SNLI and ETPA models by 91.9%. |
Explaining NLP Models via Minimal Contrastive Editing (MiCE) (2021.findings-acl)
Copied to clipboard
| Challenge: | Cognitive science and philosophy research has shown that human explanations are contrastive . a contrast case plays a key role in modulating what explanations can be given . |
| Approach: | They propose a method for producing contrastive explanations of model predictions . they edit models' outputs to change model outputs, and then edit them to the contrast case . |
| Outcome: | a new method produces contrastive explanations of model predictions in the form of edits . the edits are minimal and fluent, consistent with human contrastive edits. |
Rather a Nurse than a Physician - Contrastive Explanations under Investigation (2023.emnlp-main)
Copied to clipboard
| Challenge: | a recent study suggests that contrastive explanations are closer to how humans explain a decision than non-contrastive explanations. |
| Approach: | They analyze four English text-classification datasets to determine whether humans explain in contrast to alternatives. |
| Outcome: | The proposed explanations are closer to how humans explain a decision than non-contrastive explanations. |
Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences (2023.emnlp-main)
Copied to clipboard
| Challenge: | a common strategy to explain NLP predictions is to highlight salient tokens in their inputs. |
| Approach: | They propose a technique to generate contrastive phrasal highlights that explain the predictions of a semantic divergence model via phrase alignment guided erasure. |
| Outcome: | The proposed techniques match human rationales of cross-lingual semantic differences better than popular post-hoc saliency techniques and help people detect fine-grained meaning differences in human translations and critical machine translation errors. |
Contrastive Data and Learning for Natural Language Processing (2022.naacl-tutorials)
Copied to clipboard
| Challenge: | Current NLP models heavily rely on effective representation learning algorithms. |
| Approach: | This tutorial introduces contrastive learning and provides an introduction to the techniques. |
| Outcome: | This tutorial provides an introduction to the fundamentals of contrastive learning approaches and the theory behind them. |
Explaining Language Model Predictions with High-Impact Concepts (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to explain large language models (LLMs) are mostly correlational and lack causal features due to compositional nature of languages. |
| Approach: | They propose a framework to provide impact-aware explanations for large language models that are robust to feature changes and influential to the model’s predictions. |
| Outcome: | The proposed explanations improve on real and synthetic tasks and are robust to feature changes and influential to the model’s predictions. |
Prompting Contrastive Explanations for Commonsense Reasoning Tasks (2021.findings-acl)
Copied to clipboard
| Challenge: | Large pretrained language models (PLMs) can achieve near-human performance on commonsense reasoning tasks, but provide little human-interpretable evidence of the underlying reasoning they use. |
| Approach: | They propose to use large pretrained language models to generate evidence for commonsense reasoning NLP tasks . they use models to contrast alternative explanations based on key attribute(s) required to justify the correct answer . |
| Outcome: | The proposed model improves performance on two commonsense reasoning benchmarks compared to previous non-contrastive alternatives. |