用多语言大模型解决电商冷启动相关性匹配难题
CSRM-LLM: Embracing Multilingual LLMs for Cold-Start Relevance Matching in Emerging E-commerce Markets
- 用机器翻译激活大模型跨语言能力
- 通过检索增强查询理解，提升电商知识融合
- 多轮自蒸馏缓解标签错误，适合新市场快速上线
随着全球电商平台持续拓展，企业在进入新兴电商市场时面临冷启动挑战，主要源于人工标注和用户行为数据有限。本文分享了Coupang在该场景下的实践经验，提出一种冷启动相关性匹配框架CSRM-LLM，利用多语言大语言模型应对三大挑战：(1) 通过机器翻译任务激活大模型的跨语言迁移能力；(2) 通过基于检索的查询增强提升查询理解并融入电商领域知识；(3) 采用多轮自蒸馏训练策略减轻训练标签错误的影响。实验验证了CSRM-LLM及各项技术的有效性，实现真实上线部署，并带来显著线上收益：缺陷率降低45.8%，会话购买率提升0.866%。
原文摘要 · Abstract (English)
As global e-commerce platforms continue to expand, companies are entering new markets where they encounter cold-start challenges due to limited human labels and user behaviors. In this paper, we share our experiences in Coupang to provide a competitive cold-start performance of relevance matching for emerging e-commerce markets. Specifically, we present a Cold-Start Relevance Matching (CSRM) framework, utilizing a multilingual Large Language Model (LLM) to address three challenges: (1) activating cross-lingual transfer learning abilities of LLMs through machine translation tasks; (2) enhancing query understanding and incorporating e-commerce knowledge by retrieval-based query augmentation; (3) mitigating the impact of training label errors through a multi-round self-distillation training strategy. Our experiments demonstrate the effectiveness of CSRM-LLM and the proposed techniques, resulting in successful real-world deployment and significant online gains, with a 45.8% reduction in defect ratio and a 0.866% uplift in session purchase rate.
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