This work proposes an approach that integrates code-switching directly into masked language model pretraining, and introduces a multiview probabilistic translation strategy that samples candidate translations based on alignment likelihoods, applying substitutions only to unmasked tokens.
Large Language Models (LLMs) have made remarkable progress in the processing and modeling of many languages. Yet, unlike human multilinguals, they exhibit surprisingly limited cross-lingual knowledge transfer. While this limitation is well documented, its origins during multilingual training remain unclear. We pretrain...
Adam Gaber, Uriel Dolev, Elisabeth Fittschen et al.· 0 citations
This work proposes CAROT (Cross-Lingual Alignment of Representations in a Language-Agnostic Space via Optimal Transport), which consists of identifying language-specific representations in LLMs'internal states and aligning language-agnostic representations across languages at the token level by optimal transport, while...
Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala et al.· 0 citations
This work investigates parameter-efficient approaches for transferring knowledge from resource-rich source languages to low-resource target languages on Whisper, and proposes Sequential Adapter Stacking, which places a trainable target-language adapter on top of a frozen source-language adapter.
These results indicate that for multilingual models spanning typologically diverse scripts, to obtain maximum benefits, romanization should be treated as a core design choice applied at pretraining rather than a post hoc fix.
Mu-Ge Zhang, Aaron Jencks, Krishna Badikela et al.· 0 citations
A PMI-based translation metric is proposed, which is less dependent on the target language and correlates strongly with chrF, and finds that CLA with English predicts translation quality comparably to or better than source-target CLA.
Adnan Al Ali, Kathy Hämmerl, Jindrich Libovický et al.· 0 citations
CTFAlign is introduced, a lightweight, training-free approach for document-level word alignment that applies a coarse-to-fine refinement strategy that restricts the alignment search space to semantically similar regions and introduces MDPAlign, a simpler alternative that constrains alignments by position with a main di...
