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often contains inconsistencies due to ambiguous naming, outdated information,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often contains inconsistencies due to ambiguous naming, outdated information,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