标题： 利用LLM辅助查询理解的实时检索增强生成 显示英文标题

标题： Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation

摘要： 现实世界中的实时检索增强生成（RAG）系统在处理用户查询时面临重大挑战，这些查询通常存在噪声、模糊且包含多个意图。 虽然RAG通过外部知识增强了大型语言模型（LLMs），但当前系统通常难以处理此类复杂输入，因为它们通常是在更干净的数据上进行训练或评估的。 本文介绍了Omni-RAG，一种新颖的框架，旨在提高RAG系统在实时、开放领域环境中的鲁棒性和有效性。 Omni-RAG采用 LLM辅助的查询理解来通过三个关键模块预处理用户输入：（1）深度查询理解和分解，利用定制提示的LLMs对查询进行去噪（例如，纠正拼写错误）并将多意图查询分解为结构化子查询；（2）意图感知的知识检索，从语料库（即使用OpenSearch的FineWeb）中对每个子查询进行检索并聚合结果；以及（3）重新排序和生成，其中重新排序器（即BGE）在最终响应由LLM（即Falcon-10B）通过思维链提示生成之前优化文档选择。 Omni-RAG旨在通过稳健地处理复杂和噪声查询，弥合当前RAG能力与实际应用需求之间的差距，如SIGIR 2025 LiveRAG挑战所强调的需求。 显示英文摘要

摘要： Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents. While RAG enhances large language models (LLMs) with external knowledge, current systems typically struggle with such complex inputs, as they are often trained or evaluated on cleaner data. This paper introduces Omni-RAG, a novel framework designed to improve the robustness and effectiveness of RAG systems in live, open-domain settings. Omni-RAG employs LLM-assisted query understanding to preprocess user inputs through three key modules: (1) Deep Query Understanding and Decomposition, which utilizes LLMs with tailored prompts to denoise queries (e.g., correcting spelling errors) and decompose multi-intent queries into structured sub-queries; (2) Intent-Aware Knowledge Retrieval, which performs retrieval for each sub-query from a corpus (i.e., FineWeb using OpenSearch) and aggregates the results; and (3) Reranking and Generation, where a reranker (i.e., BGE) refines document selection before a final response is generated by an LLM (i.e., Falcon-10B) using a chain-of-thought prompt. Omni-RAG aims to bridge the gap between current RAG capabilities and the demands of real-world applications, such as those highlighted by the SIGIR 2025 LiveRAG Challenge, by robustly handling complex and noisy queries.