CORG：从复杂互相关联的上下文中生成答案

CORG: Generating Answers from Complex, Interrelated Contexts

April 25, 2025

作者: Hyunji Lee, Franck Dernoncourt, Trung Bui, Seunghyun Yoon

cs.AI

摘要

在现实世界的语料库中，知识经常在文档间重复出现，但由于命名模糊、信息过时或错误，往往存在不一致性，导致上下文之间形成复杂的相互关系。先前的研究表明，语言模型在处理这些复杂性时存在困难，通常孤立地关注单一因素。我们将这些关系分为四类：干扰性、模糊性、反事实性和重复性。我们的分析揭示，目前尚无单一方法能有效同时解决所有这些相互关系。因此，我们引入了上下文组织器（CORG），一个将多个上下文组织成独立处理组的框架。这一设计使模型能够高效找到所有相关答案，同时确保消歧。CORG由三个关键组件构成：图构建器、重排序器和聚合器。我们的实验结果表明，CORG在性能与效率之间实现了有效平衡，不仅超越了现有的分组方法，还达到了与计算更为密集的单上下文方法相当的结果。

English

In a real-world corpus, knowledge frequently recurs across documents but often contains inconsistencies due to ambiguous naming, outdated information, or errors, leading to complex interrelationships between contexts. Previous research has shown that language models struggle with these complexities, typically focusing on single factors in isolation. We classify these relationships into four types: distracting, ambiguous, counterfactual, and duplicated. Our analysis reveals that no single approach effectively addresses all these interrelationships simultaneously. Therefore, we introduce Context Organizer (CORG), a framework that organizes multiple contexts into independently processed groups. This design allows the model to efficiently find all relevant answers while ensuring disambiguation. CORG consists of three key components: a graph constructor, a reranker, and an aggregator. Our results demonstrate that CORG balances performance and efficiency effectively, outperforming existing grouping methods and achieving comparable results to more computationally intensive, single-context approaches.