Papers by Qingyun Chen

2 papers
CycleOIE: A Low-Resource Training Framework For Open Information Extraction (2025.coling-main)

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Challenge: Open Information Extraction (OpenIE) models rely heavily on large amounts of annotated data.
Approach: They propose a training framework that maximizes data efficiency through a cycle-consistency mechanism.
Outcome: The proposed approach improves the quality of training data by curating low-quality datasets annotated by a large language model.
Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation (2026.findings-acl)

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Challenge: Multi-agent systems (MAS) are increasingly used for open-ended idea generation . when and why collective interaction expands the solution space remains unclear .
Approach: They propose to study diversity in multi-agent systems across three bottom-up levels: model intelligence, agent cognition, and system dynamics.
Outcome: The proposed model yields diminishing diversity despite higher quality . the proposed model fails to expand diversity and causes it to collapse .

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