A Survey of Large Language Models for European Languages

Fuente: arXiv
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Autores principales: Ali, Wazir, Pyysalo, Sampo
Formato: Preprint
Publicado: 2024
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author Ali, Wazir
Pyysalo, Sampo
author_facet Ali, Wazir
Pyysalo, Sampo
contents Large Language Models (LLMs) have gained significant attention due to their high performance on a wide range of natural language tasks since the release of ChatGPT. The LLMs learn to understand and generate language by training billions of model parameters on vast volumes of text data. Despite being a relatively new field, LLM research is rapidly advancing in various directions. In this paper, we present an overview of LLM families, including LLaMA, PaLM, GPT, and MoE, and the methods developed to create and enhance LLMs for official European Union (EU) languages. We provide a comprehensive summary of common monolingual and multilingual datasets used for pretraining large language models.
format Preprint
id arxiv_https___arxiv_org_abs_2408_15040
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Survey of Large Language Models for European Languages
Ali, Wazir
Pyysalo, Sampo
Computation and Language
Large Language Models (LLMs) have gained significant attention due to their high performance on a wide range of natural language tasks since the release of ChatGPT. The LLMs learn to understand and generate language by training billions of model parameters on vast volumes of text data. Despite being a relatively new field, LLM research is rapidly advancing in various directions. In this paper, we present an overview of LLM families, including LLaMA, PaLM, GPT, and MoE, and the methods developed to create and enhance LLMs for official European Union (EU) languages. We provide a comprehensive summary of common monolingual and multilingual datasets used for pretraining large language models.
title A Survey of Large Language Models for European Languages
topic Computation and Language
url https://arxiv.org/abs/2408.15040