Papers with black box models
Plausible Extractive Rationalization through Semi-Supervised Entailment Signal (2024.findings-acl)
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| Challenge: | Abstract: Large language models are gaining widespread adoption in natural language processing tasks. |
| Approach: | They propose a semi-supervised approach to optimize for plausibility of extracted rationales by using a pre-trained natural language inference model and a supervised NLI predictor. |
| Outcome: | The proposed model outperforms unsupervised models by > 100% on a ERASER dataset. |
Towards Reverse Engineering of Language Models: A Survey (2025.findings-emnlp)
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| Challenge: | Due to the vast amounts of data and computational resources required for model development, protecting the model’s parameters and training data has become an urgent and crucial concern. |
| Approach: | They define "reverse engineering" techniques as attacks on large language models and provide an in-depth analysis of them. |
| Outcome: | The proposed attacks are described as “reverse engineering” techniques on LMs and provide an introduction to existing protective strategies. |
Let the CAT out of the bag: Contrastive Attributed explanations for Text (2022.emnlp-main)
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| 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. |