MERaLiON-TextLLM: Cross-Lingual Understanding of Large Language Models in Chinese, Indonesian, Malay, and Singlish
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 5 of its 8 references that are in the map. First posted on arXiv 21 Dec 2024.
Builds on
5 of 8 references.
References · 5
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,928
- 2023C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Modelscited by 694
- 2023SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoningcited by 94
- 2023Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLUcited by 53
Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating Itcited by 42
- 2025Batayan: A Filipino NLP benchmark for evaluating Large Language Modelscited by 5
- 2026BURMESE-SAN: Burmese NLP Benchmark for Evaluating Large Language Modelscited by 1
- 2026Words at Play: Benchmarking Audio Pun Understanding in Large Audio-Language Modelscited by 1
- 2026Words at Play: Benchmarking Audio Pun Understanding in Large Audio-Language Models19 Mar 2026
- 2026BURMESE-SAN: Burmese NLP Benchmark for Evaluating Large Language Models21 Feb 2026
- 2025The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It30 May 2025
- 2025Batayan: A Filipino NLP benchmark for evaluating Large Language Models19 Feb 2025
Similar, not cited
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