Papers by Julian Schlenker
Only for the Unseen Languages, Say the Llamas: On the Efficacy of Language Adapters for Cross-lingual Transfer in English-centric LLMs (2025.acl-srw)
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| Challenge: | Most state-of-the-art large language models (LLMs) are trained mainly on English data, limiting their effectiveness on non-English, especially low-resource, languages. |
| Approach: | They train language adapters for 13 languages and evaluate their effectiveness on downstream tasks using either task adapters or in-context learning. |
| Outcome: | The proposed language adapters improve performance for languages not seen during pretraining, but provide negligible benefit for seen languages. |