Papers by Yunke Zhao

2 papers
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition (2025.coling-main)

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Challenge: Current Large Language Models struggle with complex entity taxonomies in open domains and lack NER capabilities.
Approach: They propose a dataset to guide LLMs' generalization in Open NER under a universal entity taxonomy.
Outcome: The proposed model outperforms GPT-4 in 3 out-of-domain benchmarks across 15 datasets and 6 languages.
Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs’ Instruction Following Capability (2026.findings-acl)

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Challenge: Existing models lack the ability to adhere to instructions, resulting in suboptimal performance.
Approach: They propose an automated iterative instruction-following benchmark with integrated feedback mechanism.
Outcome: The proposed benchmark identifies erroneous components in model responses and provides feedback accurately.

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