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Junhao Liu

Junhao Liu has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Interpretability; often with Binyuan Hui, Fei Huang, Jiaxi Yang.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 5 · 20 works · 163 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 50 times in our library · ⭐ Paper Stars ranking: #7533

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 University of Petroleum, BeijingBeijing · China2025–2026 · 2 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2020 · 4 papers🎓Northwestern Polytechnical UniversityXi'an · China2018–2019 · 3 papers🎓Civil Aviation Flight University of ChinaLuocheng · China2023–2025 · 2 papers🎓Guizhou UniversityGuiyang · China2025 · 2 papers🎓Guangzhou Institute of Advanced TechnologyGuangzhou · China2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

 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).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workSoftware · 1 work

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

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. One-Shot Learning as Instruction Data Prospector for Large Language Models (2024 · 61 citations)
    Instruction tuning datasets keep growing, but larger collections often carry noisy examples, and choosing good subsets usually depends on costly trial-and-error.
  2. Revitalizing Black-Box Interpretability: Actionable Interpretability for LLMs via Proxy Models (2026)
    Perturbation-based explainers such as LIME and SHAP need thousands of model queries for each explanation. That makes them too expensive for closed API models like GPT-4o, especially when explanations are meant to guide optimization rather than just inspection. The authors build on the observation that different LLMs…

 Co-authors here

11 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 50 times (of the 149,439 papers whose bibliographies we read); 12 citing papers with a page here →.

Updated

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