arXiv:2412.01253cs.CLcs.AI2024-12被引 11

Yi-Lightning模型在多领域表现卓越,尤其擅长中文、数学和编程任务。

Yi-Lightning Technical Report

  • 采用增强型Mixture-of-Experts架构,结合优化路由与KV缓存技术。
  • 在Chatbot Arena排名第六,中文、数学等专项测试中位列2至4名。
  • 内置安全框架RAISE,覆盖训练到部署全链路,兼顾性能与可靠性。

本文介绍Yi-Lightning,我们最新的旗舰大语言模型。其在Chatbot Arena综合排名第六,尤其在中文、数学、编码及复杂提示等专项任务中表现突出,位居第2至第4名。Yi-Lightning采用改进的混合专家(MoE)架构,包含先进的专家划分与路由机制,并结合优化的KV缓存技术。开发流程涵盖全面的预训练、监督微调(SFT)和基于人类反馈的强化学习(RLHF),并设计了多阶段训练策略、合成数据构建与奖励建模方案。此外,我们提出RAISE(负责任AI安全引擎)框架,涵盖预训练、后训练与服务阶段的安全防护。依托可扩展的超算基础设施,各项创新显著降低训练、部署与推理成本,同时保持高性能。在公开学术基准上的进一步评估显示,Yi-Lightning在多数指标上媲美顶尖大模型,但传统静态基准结果与真实动态人类偏好存在明显差异,提示需重新审视现有基准对实际智能系统发展的指导意义。Yi-Lightning现已通过开发者平台开放:https://platform.lingyiwanwu.com。

原文摘要 · Abstract (English)

This technical report presents Yi-Lightning, our latest flagship large language model (LLM). It achieves exceptional performance, ranking 6th overall on Chatbot Arena, with particularly strong results (2nd to 4th place) in specialized categories including Chinese, Math, Coding, and Hard Prompts. Yi-Lightning leverages an enhanced Mixture-of-Experts (MoE) architecture, featuring advanced expert segmentation and routing mechanisms coupled with optimized KV-caching techniques. Our development process encompasses comprehensive pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF), where we devise deliberate strategies for multi-stage training, synthetic data construction, and reward modeling. Furthermore, we implement RAISE (Responsible AI Safety Engine), a four-component framework to address safety issues across pre-training, post-training, and serving phases. Empowered by our scalable super-computing infrastructure, all these innovations substantially reduce training, deployment and inference costs while maintaining high-performance standards. With further evaluations on public academic benchmarks, Yi-Lightning demonstrates competitive performance against top-tier LLMs, while we observe a notable disparity between traditional, static benchmark results and real-world, dynamic human preferences. This observation prompts a critical reassessment of conventional benchmarks' utility in guiding the development of more intelligent and powerful AI systems for practical applications. Yi-Lightning is now available through our developer platform at https://platform.lingyiwanwu.com.

大模型MoE架构安全框架中文能力

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