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Creators/Authors contains: "Zhang, Denghui"

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  1. ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available April 29, 2026
  2. ; ; ; ; ; ; ; (, Association for Computational Linguistics)
    Chiruzzo, Luis; Ritter, Alan; Wang, Lu (Ed.)
    Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance, especially when working within a limited budget. In this work, we aim to offer a third-party data valuation approach that benefits both data providers and model developers. We introduce a linearized future influence kernel (LinFiK), which assesses the value of individual data samples in improving LLM performance during training. We further propose ALinFiK, a learning strategy to approximate LinFiK, enabling scalable data valuation. Our comprehensive evaluations demonstrate that this approach surpasses existing baselines in effectiveness and efficiency, demonstrating significant scalability advantages as LLM parameters increase. 
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    Free, publicly-accessible full text available April 29, 2026
  3. ; ; ; ; ; (, IEEE Transactions on Knowledge and Data Engineering)
    Full Text Available 
  4. ; ; ; ; ; ; (, Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining)
    null (Ed.)
    Full Text Available 
  5. ; ; ; ; ; (, International ACM SiGIR Conference on Research and Development in Information Retrieval)
    Full Text Available