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.
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
Affiliations
Fields, topics and concepts
📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 workExperimental and Cognitive Psychology · 1 workPsychology · 1 work
🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksEvaluation & benchmarks · 1 workBias, fairness & safety · 1 work
Works by year
2020s
Papers
- 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… - 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. - 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. - 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
Venues
- International Conference on Computational Linguistics (COLING) ×2: COLING 2025 ×2
- Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
Cited by
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