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Chenhui Shen

Chenhui Shen has 2 papers in our library, in 2023, most of them on Language models & pretraining, Summarization; often with Liying Cheng, Xuan-Phi Nguyen, Guanzheng Chen.

2 papers in our library · 2023

OpenAlex · ORCID · h-index 6 · 11 works · 143 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 181 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4604

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

🏢Alibaba DAMO AcademyHangzhou · China2021–2024 · 6 paperscurrent🏢Alibaba GroupHangzhou · China2021–2024 · 6 paperscurrent🏢Alibaba GroupGeorge Town · Cayman Islands2022–2024 · 3 paperscurrent🎓Nanjing Tech UniversityNanjing · China2022–2024 · 2 paperscurrent🎓National University of SingaporeSingapore · Singapore2022–2023 · 4 papers

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.

 Concepts, shelves and topics

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20232 papersChenhui Shen's papers of 2023

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization (2023 · 119 citations)
    This paper asks whether LLMs such as ChatGPT and GPT-4 can take over from human annotators when judging abstractive summaries.
  2. SeaLLMs — Large Language Models for Southeast Asia (2023 · 92 citations)
    Mainstream LLMs are trained mostly on English. As a result, they handle Southeast Asian languages poorly, especially those written in non-Latin scripts, which standard BPE tokenizers break into very long token sequences. SeaLLMs are open models for Southeast Asian languages, built by adapting English-centric…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Large Language Models are Not Yet Human-Level Evaluators for Abstractive SummarizationChenhui Shen, Liying Cheng, Xuan-Phi Nguyen, et al.Findings of the Association for Computational Linguistics · 119 citations
  2. SeaLLMs — Large Language Models for Southeast AsiaXuan-Phi Nguyen, Wenxuan Zhang, Xin Li, et al.arXiv:2312.00738 · 92 citations
  3. Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization · original ↗ · no summary yet · arXiv:2305.13091 · Findings of EMNLP 2023Chenhui Shen et al.2023 · 75 citations

 Co-authors here

12 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 181 times (of the 141,840 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated

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