Quantifying the Plausibility of Context Reliance in Neural Machine Translation

Code

• github.com/inseq-team/inseq
  OfficialIn paperpytorch★ 462
• github.com/gsarti/pecore
  OfficialIn paperpytorch★ 15
• github.com/rachtibat/lrp-for-transformers
  pytorch★ 227
• github.com/rachtibat/lrp-explains-transformers
  pytorch★ 227

Abstract

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 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.

Tasks

Reproductions