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        <namePart type="given">Hele-Andra</namePart>
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    <abstract>This paper explores cost-efficient methods to adapt pretrained Large Language Models (LLMs) to new lower-resource languages, with a specific focus on Estonian. Leveraging the Llama 2 model, we investigate the impact of combining cross-lingual instruction-tuning with additional monolingual pretraining. Our results demonstrate that even a relatively small amount of additional monolingual pretraining followed by cross-lingual instruction-tuning significantly enhances results on Estonian. Furthermore, we showcase cross-lingual knowledge transfer from high-quality English instructions to Estonian, resulting in improvements in commonsense reasoning and multi-turn conversation capabilities. Our best model, named Llammas, represents the first open-source instruction-following LLM for Estonian. Additionally, we publish Alpaca-est, the first general task instruction dataset for Estonia. These contributions mark the initial progress in the direction of developing open-source LLMs for Estonian.</abstract>
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%0 Conference Proceedings
%T Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer
%A Kuulmets, Hele-Andra
%A Purason, Taido
%A Luhtaru, Agnes
%A Fishel, Mark
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F kuulmets-etal-2024-teaching
%X This paper explores cost-efficient methods to adapt pretrained Large Language Models (LLMs) to new lower-resource languages, with a specific focus on Estonian. Leveraging the Llama 2 model, we investigate the impact of combining cross-lingual instruction-tuning with additional monolingual pretraining. Our results demonstrate that even a relatively small amount of additional monolingual pretraining followed by cross-lingual instruction-tuning significantly enhances results on Estonian. Furthermore, we showcase cross-lingual knowledge transfer from high-quality English instructions to Estonian, resulting in improvements in commonsense reasoning and multi-turn conversation capabilities. Our best model, named Llammas, represents the first open-source instruction-following LLM for Estonian. Additionally, we publish Alpaca-est, the first general task instruction dataset for Estonia. These contributions mark the initial progress in the direction of developing open-source LLMs for Estonian.
%U https://aclanthology.org/2024.findings-naacl.210
%P 3309-3325

Markdown (Informal)

[Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer](https://aclanthology.org/2024.findings-naacl.210) (Kuulmets et al., Findings 2024)

• Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer (Kuulmets et al., Findings 2024)

ACL

• Hele-Andra Kuulmets, Taido Purason, Agnes Luhtaru, and Mark Fishel. 2024. Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3309–3325, Mexico City, Mexico. Association for Computational Linguistics.