SDSR: Lightweight Alternative to RAG for LLM Knowledge Retrieval
A new paper on arXiv (2604.19777) proposes Self-Describing Structured Retrieval (SDSR), a lightweight framework that addresses the Lost-in-the-Middle effect in large language models. Instead of relying on Retrieval-Augmented Generation (RAG), which requires heavy infrastructure, SDSR embeds human-authored navigational metadata at the beginning of structured data files. This exploits the LLM's primacy bias, improving retrieval precision for knowledge bases with human-defined semantic boundaries. The method is designed for large-scale knowledge navigation without the overhead of RAG.
Key facts
- arXiv paper 2604.19777 introduces SDSR
- SDSR stands for Self-Describing Structured Retrieval
- Addresses Lost-in-the-Middle effect in LLMs
- Uses human-authored navigational metadata at file primacy position
- Exploits LLM primacy bias rather than fighting it
- Designed as lightweight alternative to RAG
- Targets large-scale knowledge bases with human-defined boundaries
- Reduces infrastructure overhead compared to RAG
Entities
Institutions
- arXiv