Linhao Yu
Linhao Yu has 4 papers in our library, in 2023–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Chuang Liu, Deyi Xiong, Renren Jin.
OpenAlex · h-index 19 · 53 works · 850 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 168 times in our library · ⭐ Paper Stars ranking: #5402
Affiliations
Fields, topics and concepts
📚 Shelves 11The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksCognitive Neuroscience · 1 workDecision Sciences · 1 workManagement Science and Operations Research · 1 workNeuroscience · 1 workSafety Research · 1 workSocial Sciences · 1 work
🧭 Topics 4Research subjects and languages the work is about.Evaluation & benchmarks · 3 worksLanguage models & pretraining · 3 worksBias, fairness & safety · 1 workReasoning · 1 work
Works by year
2020s
Papers
- Evaluating Large Language Models: A Comprehensive Survey (2023 · 182 citations)
This survey from Tianjin University (Guo, Jin, Liu et al., 2023) maps the fast-growing field of evaluating large language models. - M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models (2023 · 34 citations)
Chinese large language models (LLMs) are improving quickly, but there has been no large Chinese benchmark that tests their knowledge the way the Chinese education system does. - AttnPO: Attention-Guided Process Supervision for Efficient Reasoning (2026 · 18 citations)
Reasoning models trained with RLVR tend to overthink, producing long chains of thought with little benefit. - Beyond Value Benchmarks: Measuring Value-Structure Alignment in Large Language Models via Symmetric Q-Sorts (2026 · 1 citation)
Most moral evaluations of LLMs score answers one item at a time. They do not show how a model ranks competing values against each other as a whole system. This paper addresses that gap by adapting Q methodology so that humans and models complete the same task. The authors built a 140-item English Q-set based on…
Co-authors here
- Chuang Liu ×2
- Deyi Xiong ×2
- Renren Jin ×2
- Yuqi Ren ×2
- Bojian Xiong
- Dan Shi
- Hua Ren Wu
- Jianxiang Peng
- Qingqing Lyu
- Qun Liu
- Shuaiyi Nie
- Tianyu Dong
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