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Quantifying the Plausibility of Context Reliance in Neural Machine Translation

Gabriele Sarti · Grzegorz ChrupaƂa · Malvina Nissim · Arianna Bisazza

Halle B #261
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Wed 8 May 1:45 a.m. PDT — 3:45 a.m. PDT

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 $\textit{when}$ and $\textit{which parts}$ 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 $\textbf{P}$lausibility $\textbf{E}$valuation of $\textbf{Co}$ntext $\textbf{Re}$liance (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 model translations to identify context-mediated predictions and highlight instances of (im)plausible context usage throughout generation.

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