FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| _version_ | 1866914991663218688 |
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| author | Sun, Haoran Jin, Renren Xu, Shaoyang Pan, Leiyu Supryadi Cui, Menglong Du, Jiangcun Lei, Yikun Yang, Lei Shi, Ling Xiao, Juesi Zhu, Shaolin Xiong, Deyi |
| author_facet | Sun, Haoran Jin, Renren Xu, Shaoyang Pan, Leiyu Supryadi Cui, Menglong Du, Jiangcun Lei, Yikun Yang, Lei Shi, Ling Xiao, Juesi Zhu, Shaolin Xiong, Deyi |
| contents | 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 (see https://huggingface.co/TJUNLP/FuxiTranyu-8B) and Github (see https://github.com/tjunlp-lab/FuxiTranyu). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_06273 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data Sun, Haoran Jin, Renren Xu, Shaoyang Pan, Leiyu Supryadi Cui, Menglong Du, Jiangcun Lei, Yikun Yang, Lei Shi, Ling Xiao, Juesi Zhu, Shaolin Xiong, Deyi Computation and Language 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 (see https://huggingface.co/TJUNLP/FuxiTranyu-8B) and Github (see https://github.com/tjunlp-lab/FuxiTranyu). |
| title | FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.06273 |