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Liying Cheng

Liying Cheng has 3 papers in our library, in 2021–2023, most of them on Language models & pretraining, Summarization; often with Chenhui Shen, Lidong Bing, Qingyu Tan.

3 papers in our library · 2021–2023

OpenAlex · ORCID · h-index 11 · 52 works · 551 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 271 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #3683

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

🎓Shenyang Normal UniversityShenyang · China2008–2025 · 10 paperscurrent🎓City University of MacauMacao · Macao2025 · 2 paperscurrent🏢Alibaba GroupHangzhou · China2022–2024 · 15 papers🏢Alibaba DAMO AcademyHangzhou · China2022–2024 · 14 papers🏢Alibaba GroupGeorge Town · Cayman Islands2022–2024 · 7 papers🎓Singapore University of Technology and DesignSingapore · Singapore2022–2024 · 5 papers🎓Queen's UniversityKingston · Canada2011–2022 · 4 papers🎓Northeastern UniversityShenyang · China2005–2012 · 4 papers🎓Tianjin University of Traditional Chinese MedicineTianjin · China2022–2023 · 3 papers🎓Queens UniversityDhaka · Bangladesh2022 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 9 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 · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3 worksSummarization · 1 work

 Works by year

2020s

20232 papersLiying Cheng's papers of 202320211 paperOn the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

 Papers

The most cited first (OpenAlex and our library).

  1. On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021 · 204 citations)
    Adapter-based tuning inserts small trainable modules into a frozen pretrained language model (PrLM). Earlier work valued it mainly for saving parameters. This paper asks a different question: when does it actually beat full fine-tuning? The authors use the Houlsby et al. adapter design. Each transformer layer gets…
  2. 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.
  3. 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

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

  1. On the Effectiveness of Adapter-based Tuning for Pretrained Language Model AdaptationRuidan He, Linlin Liu, Hai Ye, et al.Annual Meeting of the Association for Computational Linguistics (ACL) · 204 citations
  2. 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
  3. SeaLLMs — Large Language Models for Southeast AsiaXuan-Phi Nguyen, Wenxuan Zhang, Xin Li, et al.arXiv:2312.00738 · 92 citations
  4. 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
  5. M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework · original ↗ · no summary yet · Proceedings of the 2025 Conference on Empirical Methods in Natural Language…Yew Ken Chia et al.2025 · 21 citations

 Co-authors here

12 of 16 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 271 times (of the 141,840 papers whose bibliographies we read); 28 citing papers with a page here →.

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