Two papers use LLMs to improve retrieval indexing and grounded answers
_reachsumit · x · 2026-07-22
Two retrieval papers use LLMs to improve indexing and grounded generation
The post highlights two research works:
- AutoIndex: Learning Representation Programs for Retrieval
- Treats document representation as code that can be optimized.
- Uses LLM agents to diagnose retrieval failures and synthesize better indexing programs.
- The setup is for a fixed BM25 retriever.
- RAGnRoll: Learning to Iteratively Retrieve and Generate Attributable Answer Snippets
- Trains an LLM with multi-round decomposition and subquery planning.
- The goal is to generate answer snippets that are both grounded and attributed.
Both papers focus on making retrieval systems more adaptive and on improving the faithfulness of generated answers.
Related event: AutoIndex Framework Proposes Optimization via Representation Programs(8 posts)→
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