MERaLiON-TextLLM: Cross-Lingual Understanding of Large Language Models in Chinese, Indonesian, Malay, and Singlish

Xin Huang, Tarun Kumar Vangani, Minh Duc Pham, Xunlong Zou, Bin Wang +2 more
12/21/2024
cs.CLcs.AI

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

Multilingual large language models (MLLMs) have shown impressive capabilities across a variety of languages. However, efficacy can differ greatly between different language families, especially for those with limited linguistic resources. This report presents MERaLiON-TextLLM, a series of open-source language models specifically tailored to improve understanding and generation in Chinese, Indonesian, Malay, and Singlish. The initial released model is built on Llama-3-8B-Base and refined through a meticulously crafted process of continued pre-training and weight merging. Our approach achieves performance improvements across benchmarks in these languages, exceeding the capabilities of the official Llama-3 models. We provide the model checkpoints as a resource to support further research and development in cross-lingual language understanding.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{huang2024meraliontextllm,
  title  = {MERaLiON-TextLLM: Cross-Lingual Understanding of Large Language Models in Chinese, Indonesian, Malay, and Singlish},
  author = {Xin Huang and Tarun Kumar Vangani and Minh Duc Pham and Xunlong Zou and Bin Wang and Zhengyuan Liu and Ai Ti Aw},
  year   = {2024},
  eprint = {2501.08335},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.08335v3}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2501.08335.svg)](https://codeofpaper.com/paper/2501.08335)

Discussion