CORG: Generating Answers from Complex, Interrelated Contexts

• URL: http://arxiv.org/abs/2505.00023v1
• Date: Fri, 25 Apr 2025 02:40:48 GMT
• Title: CORG: Generating Answers from Complex, Interrelated Contexts
• Authors: Hyunji Lee, Franck Dernoncourt, Trung Bui, Seunghyun Yoon,
• Abstract summary: In a real-world corpus, knowledge frequently recurs across documents but often contains inconsistencies due to ambiguous naming, outdated information, or errors.<n>Previous research has shown that language models struggle with these complexities, typically focusing on single factors in isolation.<n>We introduce Context Organizer (CORG), a framework that organizes multiple contexts into independently processed groups.
• Score: 57.213304718157985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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corpus, knowledge frequently recurs across documents but often contains inconsistencies due to ambiguous naming, outdated information, or errors, leading to complex interrelationships between contexts.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.

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