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Zheni Zeng

Zheni Zeng has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining; often with Chaojun Xiao, Zhiyuan Liu, Chen Chen.

3 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 7 · 37 works · 479 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 218 times in our library · ⭐ Paper Stars ranking: #4370

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

🎓Tsinghua UniversityBeijing · China2020–2026 · 24 paperscurrent🎓Nanjing UniversityNanjing · China2025–2026 · 7 papersEmploymentcurrent🎓Nanjing University of Science and TechnologyNanjing · China2026 · 1 papercurrent🔬Beijing National Research Center for Information Science and TechnologyBeijing · China2021–2025 · 2 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.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksControl and Systems Engineering · 1 workEngineering · 1 workArts and Humanities · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3 works

 Works by year

2020s

20261 paper20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Tool Learning with Foundation Models (2023 · 383 citations)
    This survey asks how large pretrained models can learn to operate external tools such as search engines, calculators, databases, image generators and robots.
  2. ConPET: Continual Parameter-Efficient Tuning for Large Language Models (2023 · 14 citations)
    Adapting large language models to a stream of new tasks is costly when every update changes all weights. Full fine-tuning also tends to erase skills learned on earlier tasks, a problem known as catastrophic forgetting. Song et al. propose ConPET, a framework for continual task adaptation of LLMs in which training…
  3. ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling (2026 · 2 citations)
    LLMs often struggle to use retrieved external knowledge reliably. Noisy, incomplete or unfamiliar evidence can produce inconsistent answers and hallucinations. ThinkNote addresses this by treating the model as an active builder of knowledge rather than a passive reader of context, drawing on constructivist learning…

 Co-authors here

12 of 23 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 218 times (of the 149,439 papers whose bibliographies we read); 55 citing papers with a page here →.

Updated

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