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Qing Lian

Qing Lian has 2 papers in our library, in 2024, most of them on Language models & pretraining, Bias, fairness & safety; often with Tong Zhang, Hanning Zhang, Hanze Dong.

2 papers in our library · 2024

OpenAlex · h-index 7 · 15 works · 356 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 103 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6995

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 University of Science and TechnologyHong Kong · China2021–2025 · 16 paperscurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2023–2024 · 2 paperscurrent🎓University of Electronic Science and Technology of ChinaChengdu · China2019 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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

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

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. R-Tuning: Instructing Large Language Models to Say ‘I Don't Know' (2024 · 99 citations)
    The paper examines hallucinations in large language models (LLMs). The authors attribute them to the fact that standard instruction tuning forces the model to answer every question, even when the required fact is absent from its parametric knowledge. The model thus learns to guess and cannot admit that it does not…
  2. MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance (2024 · 58 citations)
    Multimodal large language models such as LLaVA can be steered into harmful output by malicious images, even when the same request in plain text would be refused.

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. Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach ↗IEEE/CVF International Conference on Computer Vision (ICCV) · no summary yetQing Lian et al.2019 · 214 citations
  2. R-Tuning: Instructing Large Language Models to Say ‘I Don't Know'Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)Hanning Zhang, Shizhe Diao, Yong Lin, et al.2024 · 99 citations
  3. MLLM-Protector: Ensuring MLLM's Safety without Hurting PerformanceConference on Empirical Methods in Natural Language Processing (EMNLP)Renjie Pi, Tianyang Han, Jianshu Zhang, et al.2024 · 58 citations

 Co-authors here

12 of 15 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 103 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated

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