Self-Evolving Search Index
SELF-INDEX enables a search index to autonomously diagnose retrieval failures and revise its keys, improving LLM agent retrieval and memory.
The paper proposes SELF-INDEX, a framework in which a retrieval index self-evolves without human intervention. An Optimizer diagnoses retrieval shortfalls, selectively revises the responsible index keys, and validates each revision before updating, while a Query Simulator proactively explores demands beyond observed queries. Across diverse corpora and retrievers, SELF-INDEX consistently improves retrieval performance, outperforming existing index optimization methods and benefiting downstream search agents and agent memory systems.
- Optimizer autonomously diagnoses and revises faulty index keys with validation
- Query Simulator explores retrieval demands beyond observed queries
- Outperforms existing index optimization across diverse corpora and retrievers
- Improves downstream search agents and agent memory retrieval
From the original article
1 min read at huggingface.co
Information retrieval is increasingly important as LLM agents tackle complex tasks involving diverse information needs. Because retrieval relies on an index that represents each document through index keys, retrieval quality depends heavily on how effectively these keys expose the knowledge contained in each document.
By Sangam Lee, Wonjae Lee, Sunghwan Kim · huggingface.co
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