Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed? (2024.findings-emnlp)
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| Challenge: | Existing models that target a single language are not seen during finetuning, but are able to respond in multiple languages once deployed in downstream applications. |
| Approach: | They investigate the minimal amount of multilinguality required during finetuning to elicit effective cross-lingual generalisation in English-centric LLMs. |
| Outcome: | The proposed model can respond in as few as two to three languages to a user's query in English, but the degree to which a target language is seen during pretraining is limiting. |
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