Papers by Jincheng Cao
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models (2025.acl-long)
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Yingshui Tan, Boren Zheng, Baihui Zheng, Kerui Cao, Huiyun Jing, Jincheng Wei, Jiaheng Liu, Yancheng He, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
| Challenge: | Large language models have created significant safety concerns . factuality ability is crucial in determining whether they can be deployed and applied safely and compliantly within specific regions. |
| Approach: | They propose a benchmark to evaluate the factuality of large language models in China . they evaluate the models' ability to provide accurate and reliable information . |
| Outcome: | The proposed benchmark evaluates the factuality abilities of existing LLMs and compares them to LLM abilities. |
NCRE: A Benchmark for Document-level Nominal Compound Relation Extraction (2025.coling-main)
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| Challenge: | Existing work focuses on detecting specific relations between entities, often constrained to specific fields and lacking general applicability. |
| Approach: | They propose a novel task that concentrates on abstract relation extraction between noun phrases . they annotate a Chinese dataset and develop a model incorporating a rotary position-enhanced word pair detection schema. |
| Outcome: | The proposed task is more efficient than previous methods. |
Breaking the Stigma! Unobtrusively Probe Symptoms in Depression Disorder Diagnosis Dialogue (2025.findings-naacl)
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| Challenge: | Recent efforts to improve depression-diagnosis-oriented dialogue systems ignore the Stigma preventing users from open conversations about their struggles. |
| Approach: | They propose a method to promote a sense of unobtrusiveness within the dialogue system and assessing depression disorder by probing symptoms. |
| Outcome: | The proposed method improves on baselines including unobtrusiveness evaluation of dialogue content and diagnostic accuracy. |