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Alexander Chapanin

2 accepted papers

2024

Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals

ICLR 2024poster

Causal explanations of the predictions of NLP systems are essential to ensure safety and establish trust. Yet, existing methods often fall short of explaining model predictions effectively or efficiently and are often model-specific. In this paper, we address model-agnostic explanations, proposing t…

Cited by 41SourcePDFScholar
2024

Measuring the Robustness of NLP Models to Domain Shifts

EMNLP 2024finding

Existing research on Domain Robustness (DR) suffers from disparate setups, limited task variety, and scarce research on recent capabilities such as in-context learning. Furthermore, the common practice of measuring DR might not be fully accurate. Current research focuses on challenge sets and relies…