Elamur Research⌕
☰
home / authors / Kehua Feng

Kehua Feng

Kehua Feng has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Chunting Zhou, Graham Neubig, Junxian He.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 4 · 23 works · 52 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 168 times in our library · ⭐ Paper Stars ranking: #5725

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

🎓Zhejiang UniversityHangzhou · China2024–2026 · 21 paperscurrent🏢Ant GroupHangzhou · China2026 · 1 papercurrent🎓Fudan UniversityShanghai · China2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSocial Sciences · 1 workSociology and Political Science · 1 workComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. FacTool: Factuality Detection in Generative AI — A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios (2023 · 160 citations)
    Large language models such as ChatGPT write fluent long-form answers that may contain factual errors. Classic fact-checking setups rarely fit this case. They usually assume that the claim to check, or the evidence for it, is already given. LLM outputs instead come without either, and they are long, with no clear unit…
  2. SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models (2024 · 36 citations)
    SciKnowEval is a benchmark for measuring how much scientific knowledge large language models hold and how well they use it.

 Co-authors here

9 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 168 times (of the 149,439 papers whose bibliographies we read); 44 citing papers with a page here →.

Updated

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