Credible plan-driven RAG method for Multi-hop Question Answering (2504.16787v1)

Abstract: Multi-hop question answering (QA) presents a considerable challenge for Retrieval-Augmented Generation (RAG), requiring the structured decomposition of complex queries into logical reasoning paths and the generation of dependable intermediate results. However, deviations in reasoning paths or errors in intermediate results, which are common in current RAG methods, may propagate and accumulate throughout the reasoning process, diminishing the accuracy of the answer to complex queries. To address this challenge, we propose the Plan-then-Act-and-Review (PAR RAG) framework, which is organized into three key stages: planning, act, and review, and aims to offer an interpretable and incremental reasoning paradigm for accurate and reliable multi-hop question answering by mitigating error propagation.PAR RAG initially applies a top-down problem decomposition strategy, formulating a comprehensive plan that integrates multiple executable steps from a holistic viewpoint. This approach avoids the pitfalls of local optima common in traditional RAG methods, ensuring the accuracy of the entire reasoning path. Subsequently, PAR RAG incorporates a plan execution mechanism based on multi-granularity verification. By utilizing both coarse-grained similarity information and fine-grained relevant data, the framework thoroughly checks and adjusts intermediate results, ensuring process accuracy while effectively managing error propagation and amplification. Experimental results on multi-hop QA datasets demonstrate that the PAR RAG framework substantially outperforms existing state-of-the-art methods in key metrics, including EM and F1 scores.

Credible Plan-Driven RAG Method for Multi-hop Question Answering: A Paradigm Analysis

The paper "Credible Plan-driven RAG method for Multi-hop Question Answering" by Ningning Zhang et al. presents an innovative approach to enhancing the reliability and accuracy of multi-hop question answering (QA) systems within Retrieval-Augmented Generation (RAG) frameworks. Multi-hop QA necessitates synthesizing information from multiple sources via intricate reasoning paths. Traditional RAG methods often fall short due to errors in reasoning paths or intermediate results, which tend to propagate and degrade the system's performance. To counter these challenges, this paper introduces PAR RAG, a framework rooted in the Plan-then-Act-and-Review (PAR) operational model, aiming to construe a systematic reasoning system through a structured cognitive framework akin to human reasoning methodologies.

Framework and Methodology

The PAR RAG Framework

PAR RAG is designed around three core and sequential stages: planning, acting, and reviewing, each of which is inspired by the PDCA (Plan-Do-Check-Act) cycle common in structured problem-solving methodologies. This cycle is particularly adept at providing clarity and error management in complex reasoning tasks. The planning stage employs a top-down decomposition strategy to create a comprehensive plan of executable reasoning steps. This strategic decomposition aligns with the nature of multi-hop QA, which thrives on coherent and interpretable reasoning paths.

Following planning, the act stage executes the plan via a structured retrieval process, incorporating both coarse and fine-grained data to maintain high information fidelity. During this stage, PAR RAG utilizes a multi-granularity verification mechanism combining semantic similarity data with rich contextual details. This thorough validation procedure mitigates error propagation through each reasoning step.

Finally, the review stage ensures that the intermediate results are dependable by referring back to the original question and integrating newly retrieved information. This cross-verification step aligns with citation practices, promoting transparency and reliability by clearly linking results with pertinent data sources.

Numerical Outcomes and Comparative Performance

Experimental results across various multi-hop QA datasets reveal that PAR RAG markedly surpasses existing methodologies, as evidenced by substantial gains in Exact Match (EM) and F1 scores. On HotpotQA and MuSiQue datasets, PAR RAG demonstrated improvements of 31.57% and 37.93% in EM scores over leading alternatives. These outcomes underscore the effectiveness of the top-down planning and multi-granularity verification model in reinforcing system accuracy against complex multi-hop queries.

Theoretical Implications

Theoretical advances introduced by the framework stem from its ability to emulate human structured reasoning, suggesting that artificial systems could adopt similar cognitive strategies to enhance their reasoning capabilities. By prioritizing comprehensive problem analysis over iterative decomposition, the system is less susceptible to errors resulting from local optima, positioning PAR RAG as a robust model for complex QA tasks.

Practical Implications and Future Scope

Practically, PAR RAG can be employed in various applications necessitating detailed decision-making, such as policy analysis or scientific research synthesis, where complex reasoning and data integration are paramount. Future explorations may focus on optimizing computational efficiency, given the method's increased token consumption and response latency.

The paper posits that improvements in LLMs' reasoning abilities remain crucial for advancing RAG efficacy. Future work could target these capabilities, juxtaposing model architectures that enhance understanding and minimize hallucination errors. Further research might also explore integrating adaptive retrieval strategies to streamline the verification process, potentially reducing computational costs.

In conclusion, this paper contributes significantly to the domain of multi-hop QA by offering a structured plan-driven approach that promises improved accuracy and reliability, emphasizing the critical role of comprehensive reasoning path planning and robust verification mechanisms.