Elamur Research⌕
☰
home / authors / Kai He

Kai He

Kai He has 2 papers in our library, in 2023–2026, most of them on Question answering, Evaluation & benchmarks, Bias, fairness & safety; often with Dongyan Zhao, Erik Cambria, Mengling Feng.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 18 · 54 works · 1,173 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 59 times in our library · ⭐ Paper Stars ranking: #8214

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

🎓National University of SingaporeSingapore · Singapore2023–2026 · 29 paperscurrent🎓Xi'an Jiaotong UniversityXi'an · China2019–2024 · 14 papers🎓Fudan UniversityShanghai · China2013–2014 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksHealth Informatics · 1 workMedicine · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Bias, fairness & safety · 1 workEvaluation & benchmarks · 1 workQuestion answering · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics (2023 · 86 citations)
    This survey asks how large language models are changing healthcare AI and what still prevents their real-world use.
  2. Curriculum Learning based Hierarchical Scoring and Analysis Framework for Question Answering Task Evaluation (2026)
    Evaluating question answering (QA) outputs from LLMs at scale is hard. Lexical metrics such as EM, F1 or BLEU miss semantic equivalence. LLM-as-a-Judge setups based on commercial APIs are slow and expensive and rarely explain why an answer failed. HiEval addresses both problems with two models trained in sequence…

 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 59 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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