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.
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