FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, Deyi Xiong
Abstract
Large language models (LLMs) have demonstrated prowess in a wide range of tasks. However, many LLMs exhibit significant performance discrepancies between high- and low-resource languages. To mitigate this challenge, we present FuxiTranyu, an open-source multilingual LLM, which is designed to satisfy the need of the research community for balanced and high-performing multilingual capabilities. The base model, FuxiTranyu-8B, features 8 billion parameters and is trained from scratch on meticulously balanced multilingual data that contains 600 billion tokens covering 43 natural languages and 16 programming languages. We also develop two instruction-tuned models: FuxiTranyu-8B-SFT which is fine-tuned on a diverse multilingual instruction dataset, and FuxiTranyu-8B-DPO which is further refined with DPO on a preference dataset for enhanced alignment ability. Extensive experiments on a wide range of multilingual benchmarks demonstrate the competitive performance of FuxiTranyu against existing multilingual LLMs, e.g., BLOOM-7B, PolyLM-13B, and Mistral-7B-Instruct. Both neuron and representation interpretability analyses reveal that FuxiTranyu achieves consistent multilingual representations across languages. To promote further research into multilingual LLMs, we release both the base and instruction-tuned FuxiTranyu models together with 58 pre-training checkpoints at HuggingFace and Github.- Anthology ID:
- 2024.emnlp-industry.110
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, US
- Editors:
- Franck Dernoncourt, Daniel Preoţiuc-Pietro, Anastasia Shimorina
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1499–1522
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.emnlp-industry.110/
- DOI:
- 10.18653/v1/2024.emnlp-industry.110
- Cite (ACL):
- Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, and Deyi Xiong. 2024. FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1499–1522, Miami, Florida, US. Association for Computational Linguistics.
- Cite (Informal):
- FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (Sun et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.emnlp-industry.110.pdf