Quantifying the Plausibility of Context Reliance in Neural Machine Translation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910365183377408 |
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| author | Sarti, Gabriele Chrupała, Grzegorz Nissim, Malvina Bisazza, Arianna |
| author_facet | Sarti, Gabriele Chrupała, Grzegorz Nissim, Malvina Bisazza, Arianna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_01188 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quantifying the Plausibility of Context Reliance in Neural Machine Translation Sarti, Gabriele Chrupała, Grzegorz Nissim, Malvina Bisazza, Arianna Computation and Language Artificial Intelligence Human-Computer Interaction Machine Learning I.2.7 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. |
| title | Quantifying the Plausibility of Context Reliance in Neural Machine Translation |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction Machine Learning I.2.7 |
| url | https://arxiv.org/abs/2310.01188 |