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Qingkai Zeng

Qingkai Zeng has 2 papers in our library, in 2021–2024, most of them on Language models & pretraining, Summarization; often with Meng Jiang, Bing Yin, Chenguang Zhu.

2 papers in our library · 2021–2024

OpenAlex · ORCID · h-index 15 · 118 works · 923 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 165 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #4332

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

🎓Nanjing UniversityNanjing · China2004–2026 · 58 paperscurrent🎓University of Notre DameNotre Dame · United States2018–2026 · 27 paperscurrent🎓Nankai UniversityTianjin · China2024–2026 · 3 paperscurrent🔬State Key Laboratory of New Technology of Computer SoftwareNanjing · China2004–2021 · 7 papers🔬IBM Research - Thomas J. Watson Research CenterYorktown Heights · United States2019–2021 · 3 papers

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.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Summarization · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20241 paperDemocratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning20211 paperEnhancing Factual Consistency of Abstractive Summarization

 Papers

The most cited first (OpenAlex and our library).

  1. Enhancing Factual Consistency of Abstractive Summarization (2021 · 188 citations)
    Abstractive summarizers often produce text that misstates or invents facts from the source. Prior work estimates that up to 30% of such outputs contain these errors, which undermines trust in them. The authors address this in two ways. First, FASum is a transformer encoder-decoder summarizer that builds a per-article…
  2. Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning (2024 · 83 citations)
    Most current approaches to personalizing large language models work by putting user information into the prompt.

All works

6 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. Enhancing Factual Consistency of Abstractive SummarizationConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Chenguang Zhu, William Hinthorn, Ruochen Xu, et al.2021 · 188 citations
  2. Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuningConference on Empirical Methods in Natural Language Processing (EMNLP)Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, et al.2024 · 83 citations
  3. Protecting Privacy in Multimodal Large Language Models with MLLMU-BenchProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · original ↗ · no summary yetZheyuan Liu et al.2025 · 22 citations
  4. MinT: Boosting Generalization in Mathematical Reasoning via Multi-view Fine-tuningProceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) · original ↗ · no summary yetZhenwen Liang et al.2024 · 19 citations
  5. Enhancing Mathematical Reasoning in LLMs by Stepwise CorrectionProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetZhenyu Wu et al.2025 · 14 citations
  6. Instant Personalized Large Language Model Adaptation via HypernetworkProceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetZhaoxuan Tan et al.2026 · 2 citations

 Co-authors here

8 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 165 times (of the 141,840 papers whose bibliographies we read); 16 citing papers with a page here →.

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