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PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers

18 Jun 2024arXiv:2406.12430archive 2025-07-28

Myeonghwa Lee, Seonho An, Min-Soo Kim

In this paper, we conduct a study to utilize LLMs as a solution for decision making that requires complex data analysis. We define Decision QA as the task of answering the best decision, d_(best), for a decision-making question Q, business rules R and a database D. Since there is no benchmark that can examine Decision QA, we propose Decision QA benchmark, DQA. It has two scenarios, Locating and Building, constructed from two video games (Europa Universalis IV and Victoria 3) that have almost the same goal as Decision QA. To address Decision QA effectively, we also propose a new RAG technique called the iterative plan-then-retrieval augmented generation (PlanRAG). Our PlanRAG-based LM generates the plan for decision making as the first step, and the retriever generates the queries for data analysis as the second step. The proposed method outperforms the state-of-the-art iterative RAG method by 15.8% in the Locating scenario and by 7.4% in the Building scenario, respectively. We release our code and benchmark at https://github.com/myeon9h/PlanRAG.

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building_dataloader myeon9h/PlanRAG/src/data_loaders/building.py official repository ran MIT (permissive) · ba6c7ed4a5b6a3f6 · report
building_fewshot myeon9h/PlanRAG/src/data_loaders/fewshot_loader.py official repository ran MIT (permissive) · 901a9d19750450ea · report
fake_tool myeon9h/PlanRAG/src/experimental_multiAgents.py official repository ran fingerprinted MIT (permissive) · 991d88a09372efdf · report
locating_dataloader myeon9h/PlanRAG/src/data_loaders/locating.py official repository ran MIT (permissive) · c5c5ae6bd284d2f1 · report
locating_fewshot myeon9h/PlanRAG/src/data_loaders/fewshot_loader.py official repository ran MIT (permissive) · 4ed499e188f19a07 · report
fewshot_loader myeon9h/PlanRAG/src/data_loaders/fewshot_loader.py official repository unverified MIT (permissive) · 9cffe3c7edd1d9d3 · report

Tasks

Decision MakingRAGRetrievalRetrieval-augmented Generation

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Methods

AdamAttentionAttention DropoutBARTBERTBPEDense ConnectionsDropoutLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionRAGResidual ConnectionSoftmaxWeight DecayWordPiece

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