← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-industry.149 · 记录 #1
Index-Time Prefix Injection for Multi-Tenant Retrieval: Improving Search Relevance Without Model Fine-Tuning
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:06
摘要
Multi-tenant enterprise search platforms serve hundreds of customers through a single shared retrieval model. Fine-tuning on individual customer data is typically prohibited by contractual and regulatory constraints, and maintaining per-customer models does not scale. We present index-time prefix injection, a training-free method that improves retrieval relevance by prepending domain-descriptive natural-language prefixes to documents during indexing. For example, prepending “IT service management knowledge article:” to an IT knowledge base shifts its embeddings into a tighter, more domain-coherent region of the vector space. Prefixes are discovered through a tiered strategy: LLM-based generation from document samples when data policies allow, domain-expert curation when they do not, and a standardized prefix library as fallback. Deployed across 18 languages and 400+ customer instances, the approach yields 3–8% Hit@5 improvements with zero model training. A/B tests confirm a 4.2% CTR lift. We describe the system design, evaluation at scale, and deployment lessons including failure modes.
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记录 #1 · 2026-07 · 最新观测版本
Index-Time Prefix Injection for Multi-Tenant Retrieval: Improving Search Relevance Without Model Fine-Tuning
Multi-tenant enterprise search platforms serve hundreds of customers through a single shared retrieval model. Fine-tuning on individual customer data is typically prohibited by contractual and regulatory constraints, and maintaining per-customer models does not scale. We present index-time prefix injection, a training-free method that improves retrieval relevance by prepending domain-descriptive natural-language prefixes to documents during indexing. For example, prepending “IT service management knowledge article:” to an IT knowledge base shifts its embeddings into a tighter, more domain-coherent region of the vector space. Prefixes are discovered through a tiered strategy: LLM-based generation from document samples when data policies allow, domain-expert curation when they do not, and a standardized prefix library as fallback. Deployed across 18 languages and 400+ customer instances, the approach yields 3–8% Hit@5 improvements with zero model training. A/B tests confirm a 4.2% CTR lift. We describe the system design, evaluation at scale, and deployment lessons including failure modes.
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