RIT-RAG: Navigating Document Corpora with Retrieval-Induced Trees
Retrieval-augmented generation (RAG) grounds language models in external corpora.
ProofPaper ↗
Key points
- Agentic RAG enables iterative search, yet exposes the model to isolated chunks without document structure, making it difficult to distinguish relevant evidence from chunks that merely resemble the query.
- Structure-aware methods such as PageIndex navigate document structure but cannot scale to the structures of large corpora, which do not fit in the LLM context.
- We propose RIT-RAG (Retrieval-Induced Tree RAG), which combines content retrieval with structural navigation.
- Offline, RIT-RAG builds a tree for each document from its table of contents or sitemap.
Sources (1)
- [1]RIT-RAG: Navigating Document Corpora with Retrieval-Induced TreesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 07:03 AM
Retrieval-augmented generation (RAG) grounds language models in external corpora.
Agentic RAG enables iterative search, yet exposes the model to isolated chunks without document structure, making it difficult to distinguish relevant evidence from chunks that merely resemble the query.
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