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Xuan Luo

Xuan Luo has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Bias, fairness & safety; often with Jing Li, Mao Zheng, Mingyang Song.

4 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 32 · 207 works · 3,636 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 29 times in our library · ⭐ Paper Stars ranking: #8498

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 38 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models (2025 · 20 citations)
    Popular long-context tests such as needle-in-a-haystack ask a model to find one inserted sentence. Recent LLMs score almost perfectly on them, so these tests no longer separate strong models from weak ones. The authors propose Counting-Stars, a benchmark that measures how well a model gathers many scattered pieces of…
  2. Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study (2025 · 9 citations)
    LLM judges are increasingly used to grade other models, but their scores can be unstable and skewed by hidden biases.
  3. A Simple and Efficient Learning-Style Prompting for LLM Jailbreaking (2026)
    The paper identifies a safety gap in current LLMs: harmful requests rewritten to look like ordinary educational questions often get past alignment safeguards.
  4. AEQ-Bench: Measuring Empathy of Omni-Modal Large Models (2026)
    Omni-modal large models (OLMs) increasingly talk to users through speech, yet evaluations of empathy usually turn audio into transcripts and ignore how something is said.

 Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 29 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

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