Qianqian Zhuang — accepted AI-conference papers

2025

InteGround: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding

Cheng Jiayang, Qianqian Zhuang, Haoran Li, Chunkit Chan, Xin Liu, Lin Qiu +1

EMNLP 2025

Grounding large language models (LLMs) in external knowledge sources is a promising method for faithful prediction. While existing grounding approaches work well for simple queries, many real-world information needs require synthesizing multiple pieces of evidence. We introduce “integrative groundin

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2024

ECON: On the Detection and Resolution of Evidence Conflicts

Cheng Jiayang, Chunkit Chan, Qianqian Zhuang, Lin Qiu, Tianhang Zhang, Tengxiao Liu +4

EMNLP 2024main

The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems, leading to the prevalence of AI-generated content and challenges in detecting misinformation and managing conflicting information, or “inter-evidence conflicts.” This study in…

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