解决电商大模型幻觉与安全漏洞，提升搜索精准与可信度。
SIA: A Synthesize-Inject-Align Framework for Knowledge-Grounded and Secure E-commerce Search LLMs with Industrial Deployment
- 合成知识与行为数据，构建高质量训练语料。
- 参数高效预训练使模型兼顾领域知识与通用能力。
- 双路径对齐提升搜索效果与抗攻击能力，适合工业级部署。
大语言模型为电商搜索带来意图感知推荐的潜力，但其工业应用受限于两大挑战：一是动态细粒度商品知识编码不足导致的知识幻觉，二是越狱攻击引发的安全漏洞威胁合规性。为此，我们提出SIA——一种合成-注入-对齐框架，用于构建具备知识与安全性的电商搜索大模型。首先，通过融合结构化知识图谱与非结构化行为日志，结合推理链和安全增强数据，合成高质量自然语言语料。其次，采用基于深度上扩的参数高效预训练策略，在注入领域知识的同时保持通用能力。最后，通过多任务指令微调与对抗训练的双路径对齐方法，强化任务性能与安全鲁棒性。该框架已在京东（中国最大自营电商平台）部署，五类核心搜索场景的A/B测试显示关键业务指标显著提升，验证了其工业有效性与可扩展性。
原文摘要 · Abstract (English)
Large language models offer transformative potential for e-commerce search by enabling intent-aware recommendations. However, their industrial deployment is hindered by two critical challenges: (1) knowledge hallucination due to insufficient encoding of dynamic, fine-grained product knowledge, and (2) security vulnerabilities under jailbreak attacks that threaten compliance. To address these issues, we propose SIA--a Synthesize-Inject-Align framework for building knowledgeable and secure e-commerce search LLMs. Our approach first synthesizes high-quality natural language corpus by combining structured knowledge graphs with unstructured behavioral logs, augmented with reasoning chains and safety-aware data. We then introduce a parameter-efficient pre-training strategy based on Depth Up-Scaling to inject domain knowledge while preserving general capabilities. Finally, a dual-path alignment method via multi-task instruction tuning and adversarial training strengthens both task performance and safety robustness. The framework has been deployed at JD.com, China's largest self-operated e-commerce platform, where A/B tests across five core search scenarios demonstrate significant improvements in key business metrics, validating its industrial effectiveness and scalability.
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