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Jingwen Pu

Jingwen Pu has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Zhaokun Wang, Jie Ou, Jinyu Guo.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 1 · 13 works · 4 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #12057

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

🎓University of Electronic Science and Technology of ChinaChengdu · China2025–2026 · 10 paperscurrent🎓Zhejiang UniversityHangzhou · China2026 · 1 papercurrent

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

 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 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. CAP: Controllable Alignment Prompting for Unlearning in LLMs (2026)
    LLMs trained on uncurated data can memorize sensitive or hazardous facts. Most unlearning techniques remove such knowledge by editing weights, which is costly, can damage general ability, and does not work for closed-source models. CAP takes a different route: the target LLM stays frozen, and a small language model…
  2. Decoding-Unlearning: Fact Forgetting via Entropy-Guided Inference (2026)
    Large language models memorize copyrighted, private, or hazardous facts. Most unlearning methods remove them by fine-tuning, which is expensive and irreversible and requires forget and retain datasets. Pu et al. propose SEGUE, which forgets at inference time with no training and no change to weights. It has two stages:

 Co-authors here

6 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.

Updated

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