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Shumin Deng

Shumin Deng has 3 papers in our library, in 2022–2024, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Ningyu Zhang, Huajun Chen, Yunzhi Yao.

3 papers in our library · 2022–2024

OpenAlex · ORCID · Wikidata · h-index 26 · 77 works · 2,921 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 307 citations in our library · 3 more in our library without a page yet · ⭐ Paper Star rank #3843

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

🎓Zhejiang UniversityHangzhou · China2017–2026 · 46 paperscurrent🎓National University of SingaporeSingapore · Singapore2022–2025 · 20 papers🏢Alibaba GroupHangzhou · China2018–2021 · 9 papers🏢Ant GroupHangzhou · China2023–2025 · 2 papers🏢Guangdong Power Grid CompanyGuangzhou · China2023–2024 · 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.

 Concepts, shelves and topics

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

📚 Shelves 8 Computer Science · 3 works Artificial Intelligence · 3 works Arts and Humanities · 2 works Language and Linguistics · 2 works Psychology · 1 work Computer Vision and Pattern Recognition · 1 work Developmental and Educational Psychology · 1 work Social Psychology · 1 work

🧭 Topics 3 Language models & pretraining · 2 works Reasoning · 1 work Bias, fairness & safety · 1 work

 Works by year

2020s

20242 papers20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Reasoning with Language Model Prompting: A Survey (2022 · 161 citations)
    This survey maps the fast-growing work on getting large language models to reason through prompting rather than through task-specific architectures.
  2. Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View (2024 · 131 citations)
    Systems built from several large language model agents are increasingly used for hard tasks, but it is unclear how their collaboration works or whether it resembles human group behaviour.
  3. Detoxifying Large Language Models via Knowledge Editing (2024 · 84 citations)
    Safety-aligned LLMs can still be steered into harmful output when a malicious request is wrapped in a carefully engineered jailbreak prompt. The authors ask whether knowledge editing can fix the specific toxic parts of a model instead of only masking them.

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Reasoning with Language Model Prompting: A SurveyShuofei Qiao, Yixin Ou, Ningyu Zhang, et al.arXiv:2212.09597 · 161 citations
  2. Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology ViewJintian Zhang, Xin Xu, Ningyu Zhang, et al.Annual Meeting of the Association for… · 131 citations
  3. Detoxifying Large Language Models via Knowledge EditingMengru Wang, Ningyu Zhang, Ziwen Xu, et al.Annual Meeting of the Association for… · 84 citations
  4. Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms (original ↗)Mengru Wang et al.2025 · Proceedings of the 63rd Annual Meeting of the… · 14 citations · no summary yet
  5. Text-guided Legal Knowledge Graph Reasoning (original ↗)Luoqiu Li et al.2021 · arXiv:2104.02284 · 13 citations · no summary yet
  6. DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation (original ↗)Minzhi Li et al.2025 · Proceedings of the 31st International… · 9 citations · no summary yet

 Co-authors here

11 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 307 times (of the 141,840 papers whose bibliographies we read); 21 citing papers with a page here →.

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