提出OkraLong框架，高效处理长文本查询，兼顾准确率与成本
OkraLong: A Flexible Retrieval-Augmented Framework for Long-Text Query Processing
- 分三阶段动态调度：分析-组织-执行，灵活优化流程
- 在多个数据集上提升回答准确率，同时降低计算开销
- 适合企业文档分析、财报理解等长文本场景
大型语言模型在处理长文本查询时面临效率挑战，如企业文档分析和财务报告理解。现有方法采用长上下文处理或检索增强生成（RAG），但存在输入成本过高或信息不完整的问题。近期方案引入上下文压缩和动态检索循环，但仍会丢失关键细节或产生迭代开销。为此，我们提出OkraLong框架，通过分析器、组织器和执行器三个协同组件，实现细粒度工作流调度。分析器识别任务状态，指导组织器动态规划流程；执行器完成具体任务并输出最终答案。实验表明，OkraLong在多种数据集上均提升了回答准确率，并实现了成本效益平衡。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。