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Juan Wen

Juan Wen has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Wanli Peng, Xue Yiming.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 18 · 80 works · 977 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #13686

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

🎓China Agricultural UniversityBeijing · China2018–2026 · 58 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2026 · 1 papercurrent🔬Kunming Institute of BotanyKunming · China2026 · 1 papercurrent🎓Beijing University of Posts and TelecommunicationsBeijing · China2006–2008 · 5 papers🎓Xiamen UniversityXiamen · China2009–2023 · 4 papers🎓Central South UniversityChangsha · China2019 · 2 papers🏛️Ministry of Agriculture and Rural AffairsBeijing · China2019 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workHardware and Architecture · 1 workLanguage and Linguistics · 1 work

🧭 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. ImF: Embedding an Implicit Fingerprint in Your Large Language Models (2026)
    The paper addresses black-box ownership verification for LLMs, where a model owner can only query a suspect model through an API or UI.
  2. SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs (2026)
    Customized LLM agents served through APIs usually keep their system prompts hidden. An attacker can therefore plant a backdoor instruction there that forces a fixed answer whenever a trigger appears. Earlier prompt-based defenses can flag such poisoned inputs, but they cannot restore the correct prediction.

 Co-authors here

2 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

This page in Markdown → https://md.research.elamur.ai/free/en/authors/juan-wen/