| Challenge: | Existing models are often used as black boxes to adapt to new domains, but there is no single recipe for making them work. |
| Approach: | They propose to use black box models to improve their performance on new domains by leveraging explanations of their behavior. |
| Outcome: | The proposed method improves model generalization performance on two tasks using explanations. |
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Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates (2022.emnlp-main)
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| Challenge: | Existing methods to improve confidence calibration of pre-trained language models are still a mystery. |
| Approach: | They propose a method that leverages model explanations to make models less confident with non-inductive attributions. |
| Outcome: | The proposed method improves confidence calibration in all settings and reduces calibration errors when combined with temperature scaling. |
SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)
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| 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. |
Learning to Explain: Generating Stable Explanations Fast (2021.acl-long)
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| Challenge: | Existing methods for explaining outcome of machine learning models produce explanations, or rationales, which identify the attributions of features in an input example. |
| Approach: | They propose a Learning to Explain approach that learns the behaviour of an underlying explanation algorithm simultaneously from all training examples. |
| Outcome: | The proposed approach is 5 to 7.5104 times faster than existing models and has comparable faithfulness to the black-box model. |
InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations (2023.findings-emnlp)
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| Challenge: | Recent work on NLP explainability methods lacks a dialogue-based interpretability framework that can convey faithful explanations in human-understandable terms. |
| Approach: | They adapt the conversational explanation framework TalkToModel to the NLP domain and add new NLP-specific operations such as free-text rationalization to illustrate its generalizability. |
| Outcome: | The proposed framework can be used to explain models on three NLP tasks and is generalizable to different datasets, use cases and models. |
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. |
| 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. |
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. |
CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Models (2023.emnlp-main)
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| Challenge: | Methods for adapting language models to new tasks and domains have traditionally assumed white-box access to the model and work by modifying its parameters. |
| Approach: | They propose a method for adapting large language models to new domains and tasks . they fine-tune a small white-box LM and combine it with a large black-box model at the probability level through a network, learned on a smaller validation set. |
| Outcome: | The proposed method improves performance in all cases, while using a domain expert 23x smaller. |
Can language models learn from explanations in context? (2022.findings-emnlp)
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Andrew Lampinen, Ishita Dasgupta, Stephanie Chan, Kory Mathewson, Mh Tessler, Antonia Creswell, James McClelland, Jane Wang, Felix Hill
| Challenge: | Language Models can adapt to a few in-context examples, but without training. |
| Approach: | They examine how explanations of few-shot examples can help Language Models (LMs) explanations can improve performance even without tuning, they find . |
| Outcome: | The proposed explanations outperform hand-tuned explanations on small validation sets. |
Explanation Regeneration via Information Bottleneck (2023.findings-acl)
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| Challenge: | Recent work builds on prompt engineering to generate free-text explanations without specific training, but they lack sufficiency and conciseness due to the prompt complexity and hallucination issues. |
| Approach: | They propose to generate explanations via the information bottleneck theory by polishing the single-pass output of large pretrained language models but retaining the information that supports the contents being explained by balancing two information bottle neck objectives. |
| Outcome: | The proposed explanations are based on the information bottleneck theory . they are able to explain black-box predictions naturally and accurately . |
Interpreting Predictions of NLP Models (2020.emnlp-tutorials)
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| Challenge: | This tutorial will provide a background on interpretation techniques for neural NLP models. |
| Approach: | This tutorial will provide a background on interpretation techniques for NLP models . it will examine saliency maps, input perturbations, adversarial attacks and influence functions . |
| Outcome: | This tutorial will provide a background on interpretation techniques . examples-specific interpretations include saliency maps, input perturbations, adversarial attacks, influence functions . |