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Open Llama2 Model for the Lithuanian Language

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arxiv 2408.12963 v1 pith:GVYDUMAE submitted 2024-08-23 cs.CL cs.AIcs.LG

classification cs.CLcs.AIcs.LG
keywords llmsopenlanguageproposedaccompanyingbenchmarkslithuanianllama2
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we propose and describe the first open Llama2 large language models (LLMs) for the Lithuanian language, including an accompanying question/answer (Q/A) dataset and translations of popular LLM benchmarks. We provide a brief review of open regional LLMs and detailed information on the proposed LLMs and their training process. We also conduct an empirical evaluation, comparing the perplexities of the proposed LLMs with those of other modern open LLMs. In addition, benchmarking the proposed LLMs against language understanding tasks reveals that high-quality pretraining datasets may be essential for achieving models that perform efficiently on these benchmarks. The full realisations of the described LLMs are available in the accompanying open repository~\url{https://huggingface.co/neurotechnology}.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Localizing AI: Evaluating Open-Weight Language Models for Languages of Baltic States

    cs.CL 2025-01 conditional novelty 5.0 of 10

    Gemma 2 open-weight models nearly match commercial AI on Baltic-language tasks, but all tested open-weight multilingual models still make frequent lexical errors in generated text.

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