OkraLong：一种用于长文本查询处理的灵活检索增强框架
OkraLong: A Flexible Retrieval-Augmented Framework for Long-Text Query Processing
AI总结：
针对LLM处理长文本查询时成本高、信息易缺失的问题，提出OkraLong框架，通过分析器、组织器和执行器三个组件实现细粒度动态编排，在提升答案准确性的同时兼顾成本效益。
摘要：
大型语言模型（LLMs）在高效处理长文本查询时面临挑战，这在企业文档分析和财务报告理解等应用中尤为明显。尽管传统方案采用长上下文处理或检索增强生成（RAG），但它们存在输入开销过高或信息不完整的问题。近期的进展采用了上下文压缩和动态检索循环，但仍然会牺牲关键细节或产生迭代成本。为了解决这些局限性，我们提出了OkraLong，一种灵活优化整个处理工作流的新型框架。与以往的静态或粗粒度自适应策略不同，OkraLong通过三个协同组件实现细粒度编排：分析器、组织器和执行器。分析器负责刻画任务状态，指导组织器动态调度工作流。执行器负责执行操作并生成最终答案。实验结果表明，OkraLong不仅提高了答案准确性，还在多种数据集上实现了成本效益。
英文摘要：
Large Language Models (LLMs) encounter challenges in efficiently processing long-text queries, as seen in applications like enterprise document analysis and financial report comprehension. While conventional solutions employ long-context processing or Retrieval-Augmented Generation (RAG), they suffer from prohibitive input expenses or incomplete information. Recent advancements adopt context compression and dynamic retrieval loops, but still sacrifice critical details or incur iterative costs. To address these limitations, we propose OkraLong, a novel framework that flexibly optimizes the entire processing workflow. Unlike prior static or coarse-grained adaptive strategies, OkraLong adopts fine-grained orchestration through three synergistic components: analyzer, organizer and executor. The analyzer characterizes the task states, which guide the organizer in dynamically scheduling the workflow. The executor carries out the execution and generates the final answer. Experimental results demonstrate that OkraLong not only enhances answer accuracy but also achieves cost-effectiveness across a variety of datasets.
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。
