Compass-v3：面向东南亚多语言电商的领域专用大语言模型规模化
Compass-v3: Scaling Domain-Specific LLMs for Multilingual E-Commerce in Southeast Asia
AI总结：
本文提出Compass-v3，一个245B参数的MoE模型，通过优化架构和OTPO对齐方法，在东南亚多语言电商任务上超越多个大模型，并已大规模部署于Shopee平台。
摘要：
大语言模型（LLM）在通用领域应用中表现出色，但在需要领域知识的专门任务中，其性能往往会下降。电商领域尤其具有挑战性，因为其数据嘈杂、异构、多语言且高度动态。我们提出了Compass-v3，一个垂直领域的混合专家（MoE）模型，总参数量为245B，每个token激活71B参数，专为东南亚电商设计。Compass-v3采用更少但更大的专家，并结合硬件高效优化——如节点内专家并行和定制的memcpy算子——以最大化GPU利用率。该模型在12T tokens的精选多语言语料库和大规模合成电商指令上，使用混合训练策略进行训练。为了增强对齐，我们提出了最优传输直接偏好优化（OTPO），该方法捕捉token级差异，并提升电商特定场景中的指令遵循能力。大量评估表明，Compass-v3在电商性能上达到了最先进水平，超越了DeepSeek-V3.1、GPT-4系列和Qwen3-235B。此外，Compass-v3在低资源东南亚语言（印度尼西亚语、泰语、菲律宾语、越南语、马来语、他加禄语）和葡萄牙语上展现出强大的多语言能力，同时在通用基准上保持有竞争力的性能。它已被广泛应用于Shopee的工业级电商平台，并正逐步取代OpenAI的流量，目前占LLM总使用量的70%以上，凸显了其在专业电商知识和广泛语言能力方面的双重优势。
英文摘要：
Large language models (LLMs) excel in general-domain applications, yet their performance often degrades in specialized tasks requiring domain-specific knowledge. E-commerce is particularly challenging, as its data are noisy, heterogeneous, multilingual, and highly dynamic. We present Compass-v3, a vertical-domain Mixture-of-Experts (MoE) model with 245B total parameters and 71B active per token, designed for Southeast Asian e-commerce. Compass-v3 adopts fewer but larger experts, combined with hardware-efficient optimizations-such as intra-node expert parallelism and a customized memcpy operator-to maximize GPU utilization. The model is trained on 12T tokens of curated multilingual corpora and large-scale synthetic e-commerce instructions using a mixed-training strategy. To enhance alignment, we propose Optimal-Transport Direct Preference Optimization (OTPO), which captures token-level distinctions and improves instruction adherence in commerce-specific scenarios. Extensive evaluations demonstrate that Compass-v3 delivers state-of-the-art e-commerce performance, surpassing DeepSeek-V3.1, GPT-4 series, and Qwen3-235B. Moreover, Compass-v3 demonstrates strong multilingual capability across low-resource Southeast Asian languages (Indonesian, Thai, Filipino, Vietnamese, Malay, Taglog) and Portuguese while sustaining competitive performance on general benchmarks. It has already been widely applied in Shopee's industrial-scale e-commerce platform and is gradually replacing OpenAI's traffic, now accounting for over 70\% of total LLM usage, highlighting its dual strengths in specialized commerce expertise and broad linguistic competence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
