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Zi Liang

Zi Liang has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Haiqin Yang, Kaishun Wu, Minxin Du.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 8 · 65 works · 174 citations OpenAlex, 2026-10-14

📄 2 works in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #14145

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

🎓Hong Kong Polytechnic UniversityHong Kong · China2024–2026 · 22 paperscurrent🏢NetEaseBeijing · China2024–2026 · 5 paperscurrent🎓Leshan Normal UniversityLeshan · China2014–2022 · 12 papers🎓Xi'an Jiaotong UniversityXi'an · China2023–2025 · 5 papers🎓Shanghai Jiao Tong UniversityShanghai · China2025 · 2 papers🎓Southwest Minzu UniversityChengdu · China2007–2008 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 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 5The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality (2026)
    Large language models often react strongly to small wording changes in a prompt. Prior work mostly treats this as a black-box optimization problem or examines whole templates, so it does not explain which words make a prompt dependable. The authors instead analyze prompt sensitivity statistically at the n-gram token…
  2. From Domains to Instances: Dual-Granularity Data Synthesis for LLM Unlearning (2026)
    Evaluating machine unlearning in LLMs is unreliable when forget sets do not match what the model actually knows.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. How Vital is the Jurisprudential Relevance: Law Article Intervened Legal Case Retrieval and Matching ↗arXiv:2502.18292 · no summary yetNuo Xu et al.2025 · 1 citation
  2. Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on QualityFindings of the Association for Computational LinguisticsQipeng Xie, Zi Liang, Jiafei Wu, et al.2026
  3. From Domains to Instances: Dual-Granularity Data Synthesis for LLM UnlearningFindings of the Association for Computational LinguisticsXiaoyu Xu, Minxin Du, Zitong LI, et al.2026

 Co-authors here

5 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.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/zi-liang/