Chain-of-Retrieval Augmented Generation (CoRAG)

Development: Introduced by Wang et al. (2025), CoRAG enables models to retrieve and reason over relevant information step by step before generating the final answer.

Problem: Conventional RAG methods typically perform a single retrieval step before generation, which limits their effectiveness for complex queries due to imperfect retrieval results.

Solution: CoRAG allows the model to dynamically reformulate queries based on the evolving state of information gathering. The approach:

  1. Uses rejection sampling to automatically generate intermediate retrieval chains
  2. Augments existing RAG datasets that only provide the correct final answer
  3. Employs various decoding strategies at test time to scale compute by controlling the length and number of sampled retrieval chains

Results: Experimental results show significant improvements, particularly in multi-hop question answering tasks, with more than 10 points improvement in Exact Match scores compared to strong baselines. CoRAG established state-of-the-art performance across diverse knowledge-intensive tasks on the KILT benchmark.

Implementing RAG

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