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Huan Zhao

Huan Zhao has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Information extraction, Evaluation & benchmarks; often with Changlong Yu, Jun Gao, Ruifeng Xu.

3 papers in our library · 2023–2026

OpenAlex · ORCID · Wikidata · h-index 21 · 59 works · 2,851 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 70 times in our library · ⭐ Paper Stars ranking: #6450

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

🏢4ThparadigmBeijing2021–2025 · 5 paperscurrent🎓Hong Kong University of Science and TechnologyHong Kong · China2014–2021 · 13 papers🎓Tsinghua UniversityBeijing · China2012–2022 · 3 papers🏢Alibaba GroupHangzhou · China2019 · 3 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 4Research subjects and languages the work is about.Information extraction · 2 worksLanguage models & pretraining · 2 worksEvaluation & benchmarks · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Exploring the Feasibility of ChatGPT for Event Extraction (2023 · 115 citations)
    The paper asks whether ChatGPT can perform event extraction from a prompt alone, without task-specific training data or fine-tuning.
  2. EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models (2024 · 11 citations)
    LLMs used for event extraction (EE) often break the task schema. They attach arguments that a given event type does not allow, or they invent event types that the instructions never defined. The authors argue that Supervised Fine-Tuning (SFT) handles these errors poorly. Under NLL loss, a wrong type or role may…
  3. EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026 · 3 citations)
    Math benchmarks such as GSM8K and MATH test LLMs on clean, fully specified problems. Real engineering work is different: it involves physical context, missing information, competing goals and uncertainty. EngiBench is designed to measure how LLMs handle such tasks and to diagnose where they fail. Problems from many…

 Co-authors here

8 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 70 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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