ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA
This work addresses error correction in multi-hop reasoning for QA systems, representing an incremental advancement with domain-specific impact.
The paper tackles the problem of error accumulation in multi-hop question answering by introducing ReAgent, a reversible multi-agent collaborative framework with backtracking mechanisms, which achieved an average 6% improvement over baseline models on three benchmarks.
Recent advances in large language models (LLMs) have significantly improved multi-hop question answering (QA) through direct Chain-of-Thought (CoT) reasoning. However, the irreversible nature of CoT leads to error accumulation, making it challenging to correct mistakes in multi-hop reasoning. This paper introduces ReAgent: a Reversible multi-Agent collaborative framework augmented with explicit backtracking mechanisms, enabling reversible multi-hop reasoning. By incorporating text-based retrieval, information aggregation and validation, our system can detect and correct errors mid-reasoning, leading to more robust and interpretable QA outcomes. The framework and experiments serve as a foundation for future work on error-tolerant QA systems. Empirical evaluations across three benchmarks indicate ReAgent's efficacy, yielding average about 6\% improvements against baseline models.