skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Xu,  Zhaozhuo"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; (, Curran Associates Inc.)
    Free, publicly-accessible full text available June 5, 2026
  2. ; ; ; ; (, Curran Associates Inc.)
    Free, publicly-accessible full text available June 5, 2026
  3. ; ; (, Association for Computational Linguistics)
    Free, publicly-accessible full text available April 29, 2026
  4. ; ; ; ; ; (, 19th USENIX Symposium on Operating Systems Design and Implementation)
    Free, publicly-accessible full text available June 7, 2026
  5. ; ; ; ; ; ; ; (, 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. 
    more » « less
    Free, publicly-accessible full text available April 29, 2026
  6. ; ; ; (, Association for Computational Linguistics)
    Full Text Available 
  7. ; ; ; ; ; ; ; ; ; ; et al (, The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP))
    Free, publicly-accessible full text available November 12, 2025
  8. ; ; ; ; ; ; ; ; (, JMLR.org)
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; ; ; (, Proceedings of Machine Learning Research)
    Accepted Manuscript Full Text Available
  10. ; ; ; (, Proceedings of the Sixth Conference on Machine Learning and Systems (MLSys'23))