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Zhaokun Wang

Zhaokun Wang has 3 papers in our library, in 2026, most of them on Language models & pretraining, Efficient models & compression; often with Jie Ou, Jingwen Pu, Jinyu Guo.

3 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 3 · 11 works · 20 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 1 time in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #13398

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 · China2022–2026 · 8 paperscurrent

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 · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

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

 Works by year

2020s

20263 papers

 Papers

The most cited first (OpenAlex and our library).

  1. AdapShot: Adaptive Many-Shot In-Context Learning with Semantic-Aware KV Cache Reuse (2026)
    Many-shot in-context learning (ICL) puts hundreds or thousands of demonstrations into an LLM prompt. Current methods use one fixed shot count for every query. The authors show this is a poor fit: accuracy does not rise steadily with more examples, and the best count depends on the model, the task and the individual…
  2. 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…
  3. 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:

All works

4 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. Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps ↗Findings of the Association for Computational Linguistics: ACL 2025 · no summary yetJie Ou et al.2025 · 2 citations
  2. AdapShot: Adaptive Many-Shot In-Context Learning with Semantic-Aware KV Cache ReuseAnnual Meeting of the Association for Computational Linguistics (ACL)Jie Ou, Jinyu Guo, Shiyao Guo, et al.2026
  3. CAP: Controllable Alignment Prompting for Unlearning in LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhaokun Wang, Jinyu Guo, Jingwen Pu, et al.2026
  4. Decoding-Unlearning: Fact Forgetting via Entropy-Guided InferenceAnnual Meeting of the Association for Computational Linguistics (ACL)Jingwen Pu, Mingjun Shi, Xinrui Ren, et al.2026

 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.

 Cited by

Papers of our library cite their works 1 time (of the 149,439 papers whose bibliographies we read).

Updated

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