Query understanding (QU) aims to accurately infer user intent to improve document retrieval. It plays a vital role in modern search engines. While large language models (LLMs) have made notable progress in this area, their effectiveness has primarily been studied on short, keyword-based queries. With the rise of AI-driven search, long-form queries with complex intent become increasingly common, but they are underexplored in the context of LLM-based QU. To address this gap, we introduce ReDI, a reasoning-enhanced query understanding method through decomposition and interpretation. ReDI uses the reasoning and understanding capabilities of LLMs within a three-stage pipeline. (i) It decomposes a complex query into a set of targeted sub-queries to capture the user intent. (ii) It enriches each sub-query with detailed semantic interpretations to enhance the retrieval of intent-document matching. And (iii), after independently retrieving documents for each sub-query, ReDI uses a fusion strategy to aggregate the results and obtain the final ranking. We collect a large-scale dataset of real-world complex queries from a commercial search engine and distill the query understanding capabilities of DeepSeek-R1 into small models for practical application. Experiments on public benchmarks, including BRIGHT and BEIR, show that ReDI consistently outperforms strong baselines in both sparse and dense retrieval paradigms, demonstrating its effectiveness. We release our code, generated sub-queries, and interpretations at https://github.com/youngbeauty250/ReDI.

翻译：查询理解旨在准确推断用户意图以改进文档检索，在现代搜索引擎中扮演着至关重要的角色。尽管大语言模型在该领域取得了显著进展，但其有效性主要基于简短的关键词查询进行研究。随着人工智能驱动的搜索兴起，具有复杂意图的长文本查询日益普遍，但在基于大语言模型的查询理解研究中尚未得到充分探索。为填补这一空白，我们提出了ReDI——一种通过分解与解释实现的推理增强型查询理解方法。ReDI利用大语言模型的推理与理解能力构建三阶段处理流程：（i）将复杂查询分解为一组针对性子查询以捕捉用户意图；（ii）通过详细的语义解释丰富每个子查询，以增强意图-文档匹配的检索效果；（iii）在为每个子查询独立检索文档后，采用融合策略聚合结果并生成最终排序。我们从商业搜索引擎收集了大规模真实世界复杂查询数据集，并将DeepSeek-R1的查询理解能力蒸馏至小模型以实现实际应用。在包括BRIGHT和BEIR在内的公开基准测试中，实验表明ReDI在稀疏检索与稠密检索范式下均持续优于现有基线方法，验证了其有效性。我们在https://github.com/youngbeauty250/ReDI发布了代码、生成的子查询及语义解释。