RISE：多跳问答中通过迭代自探索的推理增强
RISE: Reasoning Enhancement via Iterative Self-Exploration in Multi-hop Question Answering
AI总结：
针对多跳问答中复杂推理和检索增强生成的局限性，提出RISE框架，通过问题分解、检索后阅读和自我批评的迭代自探索提升模型推理准确性与任务表现。
摘要：
大型语言模型（LLMs）在许多领域表现出色，但在复杂推理任务上仍面临挑战，例如多跳问答（MHQA）。MHQA需要整合来自不同来源的证据，同时管理复杂的逻辑依赖，这常常导致推理错误。检索增强生成（RAG）在MHQA任务中被广泛使用，但在有效过滤噪声数据和检索所有必要证据方面面临挑战，从而限制了其在解决MHQA挑战中的有效性。为了应对这些挑战，我们提出了RISE：通过迭代自探索的推理增强，这是一个旨在通过迭代自探索增强模型推理能力的新颖框架。具体而言，RISE在解决MHQA任务时包含三个关键步骤：问题分解、检索后阅读和自我批评。通过利用持续的自探索，RISE识别准确的推理路径，迭代地自我提升模型整合证据、保持逻辑一致性的能力，并增强在MHQA任务中的表现。在多个MHQA基准上的大量实验表明，RISE显著提高了推理准确性和任务性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
