专为东南亚电商打造的2450亿参数大模型，兼顾多语言与专业性能。
Compass-v3: Scaling Domain-Specific LLMs for Multilingual E-Commerce in Southeast Asia
- 采用专家混合架构，仅用少量大专家提升效率并优化显卡利用率。
- 在12万亿词数据上训练，多语言电商任务表现超越GPT-4系列。
- 支持印尼语、泰语等低资源语言，已取代OpenAI成Shopee主用模型。
大型语言模型在通用任务中表现优异，但在需要领域知识的特定任务中性能常下降。电商数据噪声大、异构性强、多语言且动态变化，挑战尤甚。本文提出Compass-v3，一个面向东南亚电商的垂直领域混合专家（MoE）模型，总参数达2450亿，每令牌激活710亿。该模型采用更少但更大的专家，并结合节点内专家并行与定制内存拷贝操作等硬件优化，以最大化GPU利用率。模型在12万亿词的精选多语言语料和大规模合成电商指令数据上，通过混合训练策略进行训练。为增强对齐能力，提出最优传输直接偏好优化（OTPO），捕捉词级差异，提升电商场景中的指令遵循能力。大量评估显示，Compass-v3在电商任务上达到当前最佳表现，超越DeepSeek-V3.1、GPT-4系列及Qwen3-235B。其在印尼语、泰语、菲律宾语、越南语、马来语、他加禄语及葡萄牙语等低资源东南亚语言上表现强劲，同时在通用基准上保持竞争力。该模型已在虾皮（Shopee）工业级电商平台广泛应用，正逐步替代OpenAI流量，现占平台总大模型使用量超70%，彰显其在专业电商领域与多语言能力上的双重优势。
原文摘要 · Abstract (English)
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.
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