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Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation

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arxiv 2410.20753 v2 pith:YZPKBXB7 submitted 2024-10-28 cs.CL cs.LG

Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation

Prakhar Verma , Sukruta Prakash Midigeshi , Gaurav Sinha , Arno Solin , Nagarajan Natarajan , Amit Sharma This is my paper
classification cs.CL cs.LG
keywords planreasoninggenerationcontextexecutionexistingmethodsmulti-hop
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation. While existing approaches such as ReAct maintain reasoning chains within the language model's context window, we observe that this often leads to plan fragmentation and execution failures. Our key insight is that by isolating the reasoning plan as a directed acyclic graph (DAG) outside the LM's working memory, we can enable (1) systematic exploration of reasoning paths, (2) atomic subqueries enabling precise retrievals and grounding, and (3) efficiency through parallel execution and bounded context window utilization. Moreover, Plan*RAG's modular design allows it to be integrated with existing RAG methods, thus providing a practical solution to improve current RAG systems. On standard multi-hop reasoning benchmarks, Plan*RAG consistently achieves improvements over recently proposed methods such as RQ-RAG and Self-RAG, while maintaining comparable computational costs.

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Cited by 8 Pith papers

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