Papers › Quantifying the Plausibility of Context Reliance in Neural Machine Translation

Quantifying the Plausibility of Context Reliance in Neural Machine Translation

2 Oct 2023arXiv:2310.01188archive 2025-07-28

Gabriele Sarti, Grzegorz Chrupała, Malvina Nissim, Arianna Bisazza

Establishing whether language models can use contextual information in a human-plausible way is important to ensure their trustworthiness in real-world settings. However, the questions of when and which parts of the context affect model generations are typically tackled separately, with current plausibility evaluations being 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 model translations to identify context-mediated predictions and highlight instances of (im)plausible context usage throughout generation.

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inseq-team/inseq officialmentioned in papermentioned on GitHubpytorch report
gsarti/pecore officialmentioned in paperpytorchApache-2.0 report
rachtibat/lrp-explains-transformers mentioned on GitHubpytorch report
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get_src_ref_sentences gsarti/pecore/pecore/data_utils.py official repository unverified Apache-2.0 (permissive) · 2948f24003a1918c · report
load_mt_dataset gsarti/pecore/pecore/data_utils.py official repository unverified Apache-2.0 (permissive) · 01efe19adc8853c2 · report
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softmax_fn rachtibat/lrp-explains-transformers/lxt/explicit/functional.py community (archive-listed) ran licence not identified · pointer only · 5bacd40145b1bc65 · report

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