STRIDE: 用于检索增强多跳问答的策略迭代决策
STRIDE: Strategic Iterative Decision-Making for Retrieval-Augmented Multi-Hop Question Answering
AI总结：
STRIDE通过分离战略规划、动态控制和 grounded 执行，解决多跳问答中实体早 Commit 和推理依赖缺失的问题，提升推理鲁棒性与准确性。
摘要：
多跳问答（MHQA）通过检索和推理分散在多个文档中的证据来准确回答复杂查询。现有MHQA方法主要依赖迭代检索增强生成，存在两个主要问题：1）现有方法过早承诺表层实体而非底层推理结构，使问题分解高度易受词汇歧义影响。2）现有方法忽视推理步骤间的逻辑依赖，导致执行不协调。为解决这些问题，我们提出STRIDE框架，其核心是一个Meta-Planner首先构建实体无关的推理骨架以捕捉查询的抽象逻辑，从而推迟实体定位到推理结构确立之后，从而缓解因提前词汇承诺导致的歧义错误。一个监督者则以依赖意识的方式协调子问题执行，使可能的并行化和必要的顺序协调。通过动态决定是否检索新证据或从现有事实推断，避免冗余查询和错误传播，同时融合跨分支信息并重新公式化失败查询以增强鲁棒性。 grounded 事实提取和逻辑推理被委托给专门的执行模块，通过显式分离检索和推理确保准确性。我们进一步提出STRIDE-FT，一个模块化微调框架，使用自动生成的执行轨迹，无需人工标注或更强的教师模型。实验表明，STRIDE实现了稳健和准确的推理，而STRIDE-FT有效提升了开源LLM。
英文摘要：
Multi-hop question answering (MHQA) enables accurate answers to complex queries by retrieving and reasoning over evidence dispersed across multiple documents. Existing MHQA approaches mainly rely on iterative retrieval-augmented generation, which suffer from the following two major issues. 1) Existing methods prematurely commit to surface-level entities rather than underlying reasoning structures, making question decomposition highly vulnerable to lexical ambiguity. 2) Existing methods overlook the logical dependencies among reasoning steps, resulting in uncoordinated execution. To address these issues, we propose STRIDE, a framework that separates strategic planning, dynamic control, and grounded execution. At its core, a Meta-Planner first constructs an entity-agnostic reasoning skeleton to capture the abstract logic of the query, thereby deferring entity grounding until after the reasoning structure is established, which mitigates disambiguation errors caused by premature lexical commitment. A Supervisor then orchestrates sub-question execution in a dependency-aware manner, enabling efficient parallelization where possible and sequential coordination when necessary. By dynamically deciding whether to retrieve new evidence or infer from existing facts, it avoids redundant queries and error propagation, while fusing cross-branch information and reformulating failed queries to enhance robustness. Grounded fact extraction and logical inference are delegated to specialized execution modules, ensuring faithfulness through explicit separation of retrieval and reasoning. We further propose STRIDE-FT, a modular fine-tuning framework that uses self-generated execution trajectories from STRIDE, requiring neither human annotations nor stronger teacher models. Experiments show that STRIDE achieves robust and accurate reasoning, while STRIDE-FT effectively enhances open-source LLMs.
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。
