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Pengfei Hong

Pengfei Hong has 2 papers in our library, in 2020–2023, most of them on Language models & pretraining, Sentiment & emotion, Dialogue systems; often with Soujanya Poria, Alexander Gelbukh, Deepanway Ghosal.

2 papers in our library · 2020–2023

OpenAlex · h-index 9 · 16 works · 522 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 145 times in our library · ⭐ Paper Stars ranking: #3864

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

🎓Singapore University of Technology and DesignSingapore · Singapore2020–2025 · 12 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workDialogue systems · 1 workSentiment & emotion · 1 work

 Works by year

2020s

20231 paperPengfei Hong's papers of 202320201 paperPengfei Hong's papers of 2020

 Papers

The most cited first (OpenAlex and our library).

  1. MIME: MIMicking Emotions for Empathetic Response Generation (2020 · 278 citations)
    The paper tackles empathetic response generation in dialogue. The authors argue that earlier systems, such as the state-of-the-art MoEL, treat all emotions as one flat, uniform set. MIME instead assumes that empathetic listeners partly copy the speaker's emotion, and that how much they copy depends on whether that…
  2. INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models (2023 · 92 citations)
    Instruction-tuned LLMs such as GPT-4 are widely used, but their real abilities are hard to judge. Many are closed systems, and open-source releases often come with informal claims, such as matching most of ChatGPT's quality, that lack solid evidence. The authors propose INSTRUCTEVAL, an evaluation suite that tests…

 Co-authors here

7 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 145 times (of the 141,840 papers whose bibliographies we read); 20 citing papers with a page here →.

Updated

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