RISE : Reasoning Enhancement via Iterative Self-Exploration in Multi-hop Question Answering (2025)

Abstract: Large Language Models (LLMs) excel in many areas but continue to face challenges with complex reasoning tasks, such as Multi-Hop Question Answering (MHQA). MHQA requires integrating evidence from diverse sources while managing intricate logical dependencies, often leads to errors in reasoning. Retrieval-Augmented Generation (RAG), widely employed in MHQA tasks, faces challenges in effectively filtering noisy data and retrieving all necessary evidence, thereby limiting its effectiveness in addressing MHQA challenges. To address these challenges, we propose RISE:Reasoning Enhancement via Iterative Self-Exploration, a novel framework designed to enhance models’ reasoning capability through iterative self-exploration. Specifically, RISE involves three key steps in addressing MHQA tasks: question decomposition, retrieve-then-read, and self-critique. By leveraging continuous self-exploration, RISE identifies accurate reasoning paths, iteratively self-improving the model’s capability to integrate evidence, maintain logical consistency, and enhance performance in MHQA tasks. Extensive experiments on multiple MHQA benchmarks demonstrate that RISE significantly improves reasoning accuracy and task performance.

## introduction large language models (llms) demonstrate outstanding capabilities in natural language understanding and generation @xcite @xcite @xcite . however, llms still face challenges with complex multi-hop question answering (mhqa) tasks. mhqa requires models to integrate evidence from multiple sources and manage intricate logical relationships. this involves both retrieving and combining various * equal contributions. ## results and analysis in this section, we evaluate rise from three aspects. first, we validate effectiveness of multiround self-iterative and compare rise with mainstream mhqa methods. second, we conduct an in-depth analysis of the performance of question decomposition, retrieve-then-read, and self-critique using objective metrics and ai-based evaluations. finally, we conduct ablation studies to verify the importance of different tasks in enhancing performance. ## conclusion we propose rise, a framework that addresses two key errors in mhqa tasks: evidence aggregation and reasoning decomposition. through selfexploration, rise continuously enhances reasoning capabilities. additionally, rise integrates selfiterative paradigm with rag framework, bridging the gap in applying self-iterative strategies to mhqa scenarios without requiring manual intervention or reliance on large models, thereby offering a cost-effective solution. experimental results on mhqa benchmarks demonstrate significant improvements in reasoning accuracy and task performance, highlighting rise's robustness and adaptability in tackling complex reasoning challenges.