Pith. sign in

REVIEW 1 cited by

A Survey of Large Language Models for European Languages

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2408.15040 v2 pith:TBY3CWQQ submitted 2024-08-27 cs.CL

classification cs.CL
keywords languagelargellmsmodelseuropeanlanguagesadvancingattention
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Towards AI Evaluation in Domain-Specific RAG Systems: The AgriHubi Case Study

    cs.CL 2026-02 conditional novelty 4.0 of 10

    AgriHubi, a Finnish-language agricultural RAG system built on PORO models, showed improved user ratings (top scores from 3% to 21%) across two rounds of testing.

Pith tools