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Xiaoming Liu

Xiaoming Liu has 2 papers in our library, in 2024–2026; often with Chao Shen, Chengzhengxu Li, Zhaohan Zhang.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 12 · 65 works · 903 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 30 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9162

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

🎓Zhongyuan University of TechnologyZhengzhou · China2017–2026 · 20 paperscurrent🎓North China University of TechnologyBeijing · China2010–2024 · 14 papers🎓Xi'an Jiaotong UniversityXi'an · China2014–2024 · 4 papers🎓Michigan State UniversityEast Lansing · United States2013–2019 · 4 papers🎓Northeast Normal UniversityChangchun · China2016–2024 · 3 papers🏢GE Global ResearchNiskayuna · United States2008–2012 · 2 papers🎓Beijing University of TechnologyBeijing · China2010 · 2 papers🔬Los Alamos National LaboratoryLos Alamos · United States2005–2007 · 2 papers🎓Zhejiang UniversityHangzhou · China1999–2000 · 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better (2024 · 16 citations)
    The paper tackles detection of machine-generated text (MGT) and addresses three weaknesses of DetectGPT. Its random perturbations can inject noise. It reduces perturbation to a single probability-difference score. Its threshold-based logit regression generalizes poorly and fails on single or small-batch inputs. The…
  2. Confidence Should Be Calibrated More Than One Turn Deep (2026 · 3 citations)
    Research on LLM confidence calibration has mostly looked at single-turn question answering. Real deployments, however, involve dialogues where users push back. The authors define multi-turn calibration : confidence must match accuracy at every turn, given the conversation history. They introduce ECE@T , which…

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. Missing Modalities Imputation via Cascaded Residual Autoencoder ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetLuan Tran et al.2017 · 256 citations
  2. Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be BetterAnnual Meeting of the Association for Computational Linguistics (ACL)Shengchao Liu, Xiaoming Liu, Yichen Wang, et al.2024 · 16 citations
  3. Confidence Should Be Calibrated More Than One Turn DeepAnnual Meeting of the Association for Computational Linguistics (ACL)Zhaohan Zhang, Chengzhengxu Li, Xiaoming Liu, et al.2026 · 3 citations

 Co-authors here

6 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 30 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated

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