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Creators/Authors contains: "Wu, Jinjun"

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  1. ; ; (, Nature Communications)
    Free, publicly-accessible full text available December 1, 2026
  2. ; ; ; ; ; ; ; (, ACS applied materials interfaces)
    Free, publicly-accessible full text available October 29, 2026
  3. ; ; ; ; ; ; (, Proceedings of Machine Learning Research)
    Free, publicly-accessible full text available September 26, 2026
  4. (, Society of Economic Geologists 2025 Conference. Brisbane, Australia (September 26-29, 2025))
    Free, publicly-accessible full text available September 29, 2026
  5. ; ; ; (, IEEE)
    Free, publicly-accessible full text available October 27, 2026
  6. ; ; ; ; ; ; ; ; ; (, npj Spintronics)
    Free, publicly-accessible full text available December 1, 2026
  7. ; ; ; ; ; (, 2nd Workshop on Test-Time Adaptation: Putting Updates to the Test (PUT))
    As prompts become central to Large Language Models (LLMs), optimizing them is vital. Textual Stochastic Gradient Descent (TSGD) offers a data-driven approach by iteratively refining prompts using LLM-suggested updates over minibatches. We empirically show that increasing training data initially improves but can later degrade TSGD's performance across NLP tasks, while also raising computational costs. To address this, we propose Textual Stochastic Gradient Descent with Momentum (TSGD-M)—a scalable method that reweights prompt sampling based on past batches. Evaluated on 9 NLP tasks across three domains, TSGD-M outperforms TSGD baselines for most tasks and reduces performance variance. 
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    Free, publicly-accessible full text available July 19, 2026
  8. ; ; ; (, ACM Transactions on Graphics)
    Free, publicly-accessible full text available September 26, 2026
  9. ; ; ; ; (, ACM)
    The paper explores the performance of LLMs in the context of multi-dimensional analytic writing assessments, i.e. their ability to provide both scores and comments based on multiple assessment criteria. Using a corpus of literature reviews written by L2 graduate students and assessed by human experts against 9 analytic criteria, we prompt several popular LLMs to perform the same task under various conditions. To evaluate the quality of feedback comments, we apply a novel feedback comment quality evaluation framework. This framework is interpretable, cost-efficient, scalable, and reproducible, compared to existing methods that rely on manual judgments. We find that LLMs can generate reasonably good and generally reliable multi-dimensional analytic assessments. We release our corpus and code\footnote{\url{https://github.com/jaaack-wang/multi-dimensional-analytic-writing-assessments}.} for reproducibility. 
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    Free, publicly-accessible full text available August 1, 2026
  10. ; ; ; ; ; ; ; ; ; (, Nature Communications)
    Free, publicly-accessible full text available December 1, 2026