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Jun Kuang

Jun Kuang has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Bias, fairness & safety; often with Peng Ding, Xuezhi Cao, Jiajun Chen.

2 papers in our library · 2023–2026

OpenAlex · h-index 0 · 2 works · 0 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 80 times in our library · ⭐ Paper Stars ranking: #8213

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

🏢MeituanBeijing · 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 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 3Research subjects and languages the work is about.Language models & pretraining · 2 worksBias, fairness & safety · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily (2023 · 85 citations)
    Safety-aligned LLMs can still be manipulated into giving harmful answers through jailbreak prompts. Existing attacks either depend on laborious hand-written templates (e.g., DAN) or on slow gradient search over white-box models (e.g., GCG). GCG produces nonsensical suffixes that are easy to filter and work poorly on…
  2. Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs' Instruction Following Capability (2026 · 1 citation)
    Large language models often fail to satisfy every requirement in prompts that contain many constraints. Meeseeks is a benchmark that measures how far models can improve when they get repeated, targeted feedback. It works in cycles. The tested model answers a prompt, and each constraint is checked automatically. The…

 Co-authors here

9 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 80 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated

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