Retrieval-augmented generation (RAG) has emerged as a promising paradigm for enhancing large language models (LLMs) on multi-hop question answering (QA), which requires reasoning over evidence from multiple documents.
However, current multi-hop RAG approaches still struggle with evidence selection and information transfer, i.e., retrieving irrelevant passages or failing to pass intermediate information to subsequent reasoning steps, thereby hindering coherent multi-hop reasoning.
To this end, we propose ConRAG, a consensus-driven evidence flow framework that effectively boosts LLM performance on complex multi-hop QA.
In short, the core of ConRAG is to select relevant evidence via consensus-driven multi-view ranking, and leverage a slot-based mechanism to transfer intermediate information between dependent reasoning steps.
Extensive experiments across three multi-hop QA benchmarks show that ConRAG surpasses the baselines by a clear margin.
Encouragingly, on Gemma-4-31B-it, ConRAG outperforms the strongest baseline by 5.8 points in average score, while reducing total end-to-end runtime and token consumption by 75.9% and 67.3%, respectively.