title: CORG: Generating Answers from Complex, Interrelated Contexts
summary: 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  often contains inconsistencies due to ambiguous naming, outdated information,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.
paper_authors._id: 6818413000ee590453feaf66
paper_authors.name: Hyunji Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:34:41.006Z
paper_authors._id: 65169ea3fbfe82f36fc6655c
paper_authors.avatarUrl: /avatars/01714ad316a2e06488246e4fe7dcdb52.svg
paper_authors.fullname: Hyun Ji Lee
paper_authors.type: user
paper_authors.user: hyunjilee
paper_authors._id: 6818413000ee590453feaf67
paper_authors.name: Franck Dernoncourt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-05T07:30:36.382Z
paper_authors._id: 62c5947524171688a9feb992
paper_authors.avatarUrl: /avatars/5a151713b9eae8dc566f5957acee3475.svg
paper_authors.fullname: Franck Dernoncourt
paper_authors.type: user
paper_authors.user: Franck-Dernoncourt
paper_authors._id: 6818413000ee590453feaf68
paper_authors.name: Trung Bui
paper_authors._id: 6818413000ee590453feaf69
paper_authors.name: Seunghyun Yoon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:35:01.378Z
paper_authors._id: 6690ef3db70d356ed3e05cb0
paper_authors.avatarUrl: /avatars/530f3a0bd7b93e1e7f385c2708335728.svg
paper_authors.fullname: yoon seung-hyun
paper_authors.type: user
paper_authors.user: aifactoryysh
paper_title: CORG: Generating Answers from Complex, Interrelated Contexts
paper_summary: 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.
paper_ai_summary: Context Organizer (CORG) addresses complex interrelationships in a corpus by organizing multiple contexts, balancing performance and efficiency.
paper_ai_keywords: graph constructor reranker aggregator