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Chengzu Li

Chengzu Li has 2 papers in our library, in 2022–2026, most of them on Language models & pretraining; often with Caiming Xiong, Caiqi Zhang, Deqing Yang.

2 papers in our library · 2022–2026

OpenAlex · ORCID · h-index 8 · 46 works · 680 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 316 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5890

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

🎓Donghua UniversityShanghai · China2022–2026 · 18 paperscurrent🎓University of CambridgeCambridge · United Kingdom2023–2026 · 14 paperscurrent🎓Xi'an Jiaotong UniversityXi'an · China2025–2026 · 3 paperscurrent🎓University of Hong KongPok Fu Lam · China2022–2023 · 2 papers🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2022 · 2 papers🎓Yale UniversityNew Haven · United States2022 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20261 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Binding Language Models in Symbolic Languages (2022 · 170 citations)
    End-to-end neural systems perform well on NLP tasks but are hard to interpret and fragile. Symbolic programs are transparent but limited to what their grammar can express. Earlier neural-symbolic hybrids needed custom languages, task-specific modules and large training sets. The authors introduce BINDER, a framework…
  2. Confidence Estimation for LLMs in Multi-turn Interactions (2026 · 7 citations)
    Most work on LLM confidence estimation looks at single questions answered once. This paper studies a different case: multi-turn dialogue, where useful information arrives over several turns. The authors argue that a good confidence signal in this setting must do two things. It should be calibrated at each turn, and…

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. Binding Language Models in Symbolic LanguagesarXiv:2210.02875Zhoujun Cheng, Tianbao Xie, Peng Shi, et al.2022 · 170 citations
  2. Confidence Estimation for LLMs in Multi-turn InteractionsFindings of the Association for Computational LinguisticsCaiqi Zhang, Ruihan Yang, Xiaochen Zhu, et al.2026 · 7 citations
  3. LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetCaiqi Zhang et al.2026

 Co-authors here

12 of 17 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 316 times (of the 149,439 papers whose bibliographies we read); 46 citing papers with a page here →.

Updated

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