llm-retrieval

SDSR proposes lightweight self-describing structured data with dual-layer guidance to exploit LLM primacy bias, achieving 100% routing accuracy without vector DBs.

Systematic study shows LLM-based dense retrievers outperform BERT baselines on typos and poisoning but remain vulnerable to semantic perturbations, with embedding geometry predicting robustness.