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- W4387322849 abstract "Establishing whether language models can use contextual information in a human-plausible way is important to ensure their safe adoption in real-world settings. However, the questions of when and which parts of the context affect model generations are typically tackled separately, and current plausibility evaluations are practically limited to a handful of artificial benchmarks. To address this, we introduce Plausibility Evaluation of Context Reliance (PECoRe), an end-to-end interpretability framework designed to quantify context usage in language models' generations. Our approach leverages model internals to (i) contrastively identify context-sensitive target tokens in generated texts and (ii) link them to contextual cues justifying their prediction. We use PECoRe to quantify the plausibility of context-aware machine translation models, comparing model rationales with human annotations across several discourse-level phenomena. Finally, we apply our method to unannotated generations to identify context-mediated predictions and highlight instances of (im)plausible context usage in model translations." @default.
- W4387322849 created "2023-10-04" @default.
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- W4387322849 date "2023-10-02" @default.
- W4387322849 modified "2023-10-14" @default.
- W4387322849 title "Quantifying the Plausibility of Context Reliance in Neural Machine Translation" @default.
- W4387322849 doi "https://doi.org/10.48550/arxiv.2310.01188" @default.
- W4387322849 hasPublicationYear "2023" @default.
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