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Qianyu Wang · Xiaoman Wang · Yuanyuan Liang · Xinyuan Li · Yunshi Lan
Grammatical error correction (GEC) systems are usually trained and evaluated on GEC benchmarks, but their performance often drops sharply once the surrounding context is slightly perturbed or extended. This indicates that the existing GEC models usually fail to understand the error patterns in th...
Wenbiao Tao · Xinyuan Li · Yunshi Lan · Weining Qian
Retrieval-Augmented Generation enhances language models by retrieving external knowledge to support informed and grounded responses. However, traditional RAG methods rely on fragment-level retrieval, limiting their ability to address query-focused summarization queries. GraphRAG introduces a grap...
Yixu Wang · Xin Wang · Yang Yao · Xinyuan Li · Xibang Yang · Yan Teng · Xingjun Ma · Yingchun Wang
The rapid integration of Large Language Models (LLMs) into high-stakes domains necessitates reliable safety and compliance evaluation. However, existing static benchmarks are ill-equipped to address the dynamic nature of AI risks and evolving regulations, creating a critical safety gap. This pape...