5 papers
Retrieval & RAG
Grounding generation in external knowledge, from classic RAG to reasoning retrieval.
- Papers
- 5
- Read time
- 1.4h
- Avg length
- 17m
DeepRAG: Thinking to Retrieve Step by Step for Large Language Models
DeepRAG teaches an LLM to break a hard question into sub-questions and, for each one, decide *on its own* whether to hit a retriever or answer from memory — by treating the whole thing as a Markov Decision Process and training the model with tree search + imitation learning + preference calibration, getting ~21-26% more accurate answers while retrieving *less*.
- Read
- 18 min
- Source
- arXiv:2502.01142
Hybrid Retrieval-Augmented Generation (RAG) Systems with Embedding Vector Databases
Don't make your RAG system choose between semantic vector search and old-school keyword search — run both, fuse the rankings with query-aware weighting, and you cut hallucinations while gaining 15-35% retrieval recall over either method alone.
- Read
- 16 min
Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks
Stop thinking of RAG as "retrieve then generate" and start thinking of it as a graph of swappable LEGO bricks — modules, sub-modules, and operators — that you wire together into routing, branching, and looping flows to fit each use case.
- Read
- 22 min
MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers
Instead of picking one retriever for your RAG system, run many small cheap ones and let zero-shot geometry signals decide, per query, how much to trust each — and eight tiny models combined beat a 7B retriever while costing a fraction of the params.
- Read
- 16 min
- Source
- arXiv:2506.15862
RankCoT: Refining Knowledge for Retrieval-Augmented Generation through Ranking Chain-of-Thoughts
Instead of separately reranking documents *or* summarizing them, RankCoT trains a single LLM to write a short query-focused "reasoning note" that implicitly ranks and distills the retrieved pile — and it learns to do this from preference pairs it generates and grades against itself.
- Read
- 14 min