Summary of Plan*rag: Efficient Test-time Planning For Retrieval Augmented Generation, by Prakhar Verma et al.
Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation
by Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin, Nagarajan Natarajan, Amit Sharma
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel PlanRAG framework enables structured multi-hop reasoning in retrieval-augmented generation (RAG) by generating test-time reasoning plans. Unlike existing approaches like ReAct, which maintain reasoning chains within the language model’s context window, PlanRAG isolates the reasoning plan as a directed acyclic graph (DAG) outside the LM’s working memory. This allows for systematic exploration of reasoning paths, atomic subqueries for precise retrievals and grounding, and efficiency through parallel execution and bounded context window utilization. PlanRAG’s modular design enables integration with existing RAG methods, providing a practical solution to improve current systems. On standard multi-hop reasoning benchmarks, PlanRAG achieves improvements over recently proposed methods like RQ-RAG and Self-RAG while maintaining comparable computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PlanRAG is a new tool that helps computers do more complex thinking tasks. It’s like a plan for how to solve a problem step-by-step. Other tools don’t always work well because they get stuck in one place or can’t find the right information. PlanRAG solves this by breaking down the task into smaller, manageable parts and letting it figure out the best way to do each part. This makes it more efficient and accurate. It’s also flexible, so it can be used with other tools that already exist. In tests, Plan*RAG did better than some other methods at doing complex thinking tasks while using less computer power. |
Keywords
» Artificial intelligence » Context window » Grounding » Language model » Rag » Retrieval augmented generation
