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Xiner Li

Xiner Li has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Haoran Wang, James Caverlee, Lichao Sun.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 7 · 18 works · 311 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 204 times in our library · ⭐ Paper Stars ranking: #6105

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

🎓Shanghai University of Engineering ScienceShanghai · China2019–2025 · 7 paperscurrent🎓Jiangsu UniversityZhenjiang · China2025 · 2 paperscurrent🎓Northwestern Polytechnical UniversityXi'an · China2025 · 2 paperscurrent🎓Southeast UniversityNanjing · China2025 · 2 paperscurrent🎓Texas A&M UniversityCollege Station · United States2025 · 2 paperscurrent🎓Xiamen University of TechnologyXiamen · China2025 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksSafety Research · 1 workSocial Sciences · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksBias, fairness & safety · 1 workReasoning · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. TrustLLM: Trustworthiness in Large Language Models (2024 · 237 citations)
    TrustLLM tackles the lack of a unified way to judge whether large language models behave reliably and responsibly.
  2. Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning (2025 · 23 citations)
    RL post-training, as used for DeepSeek-R1, helps LLMs reason, but it struggles on hard problems. When a base model rarely solves a task zero-shot, correctness-only rewards are too sparse to learn from. The authors frame reasoning as generalization: learning principles on simpler problems and transferring them to…

 Co-authors here

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

 Cited by

Papers of our library cite their works 204 times (of the 149,439 papers whose bibliographies we read); 56 citing papers with a page here →.

Updated

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