PlanRAG models exploratory reasoning problems as logical query trees, uses dynamic programming with a cost model to build them, and executes iterative retrieval-generation over the trees, outperforming prior RAG methods on the new WikiWeb-ERP dataset.
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering
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RASER routers built on one-shot RAG features selectively escalate retrieval, matching SOTA F1 scores on multi-hop QA while using 41-49% of the tokens required by always-prune across six LLMs and three benchmarks.
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RASER: Recoverability-Aware Selective Escalation Router for Multi-Hop Question Answering
RASER routers built on one-shot RAG features selectively escalate retrieval, matching SOTA F1 scores on multi-hop QA while using 41-49% of the tokens required by always-prune across six LLMs and three benchmarks.