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Jinyuan Jia

Jinyuan Jia has 3 papers in our library, in 2023–2024, most of them on Language models & pretraining, Information retrieval & search, Bias, fairness & safety; often with Runpeng Geng, Bill Yuchen Lin, Binghui Wang.

3 papers in our library · 2023–2024

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

📄 3 works in our library · 🔗 Cited 227 times in our library · ⭐ Paper Stars ranking: #5616

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

🎓Pennsylvania State UniversityState College · United States2024–2026 · 12 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 3Research subjects and languages the work is about.Bias, fairness & safety · 1 workInformation retrieval & search · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding (2024 · 127 citations)
    Aligned LLMs can still be jailbroken: adversarial prompts push them into producing harmful content. Existing defenses often protect poorly, slow down inference, or make models less useful to ordinary users. The authors studied token distributions under attack and found that the compliant opening (e.g., "Sure")…
  2. Formalizing and Benchmarking Prompt Injection Attacks and Defenses (2023 · 84 citations)
    LLM-Integrated Applications build their prompts from an instruction and from data taken from outside sources such as resumes or webpages.
  3. PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models (2024 · 80 citations)
    Retrieval-Augmented Generation (RAG) lets an LLM answer questions using texts pulled from an external knowledge database.

 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 227 times (of the 149,439 papers whose bibliographies we read); 58 citing papers with a page here →.

Updated

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