70亿参数的多语言翻译模型，效果媲美闭源大模型
Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters
- 用28种语言数据预训练，再通过思维链和强化学习优化翻译能力
- 在28种语言上表现接近Gemini-2.5和GPT-4o，优于更大开源模型
- 适合研究多语言生成与开源模型落地应用的开发者
多语言翻译是大型语言模型面临的挑战，常出现语言模式复杂、译文生硬等问题。本文提出Seed-X，一个包含指令模型与推理模型的开源70亿参数多语言LLM家族，突破翻译性能边界。基础模型在涵盖28种语言的高质量单语与双语数据集上预训练，充分挖掘多语言数据潜力。指令模型通过思维链（CoT）推理进行微调，并进一步采用强化学习（RL）提升跨语言对的泛化能力。Seed-X在28种语言上的表现可与领先闭源模型（如Gemini-2.5和GPT-4o）相媲美，在自动评估与人工评测中显著优于更大规模的开源模型。我们公开了优化实践与全部参数，推动翻译研究与应用发展。
原文摘要 · Abstract (English)
Multilingual translation stands as a challenging task for large language models (LLMs) to handle intricate language patterns and stilted translations that arise in automated translations. In this paper, we introduce Seed-X, a family of open-source LLMs comprising instruct and reasoning models, pushing the limits of translation capability with 7B parameter size. The base model is pre-trained on a diverse, high-quality dataset encompassing both monolingual and bilingual content across 28 languages, harnessing the full potential of multilingual data. The instruct model is then finetuned to translate by Chain-of-Thought (CoT) reasoning and further enhanced through reinforcement learning (RL) to achieve better generalization across diverse language pairs. Seed-X achieves performance comparable to leading closed-source models, including Gemini-2.5 and GPT-4o, across 28 languages, and significantly outperforms larger open-source models in both automatic metrics and human evaluations. We share the best practices through our optimization process, and make the parameter public available for advancing translation research and applications.
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