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Mengyang Qiu

Mengyang Qiu has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 5 · 23 works · 59 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 41 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7549

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

🎓College of Saint ElizabethMorristown · United States2026 · 1 papercurrent🎓Trent UniversityPeterborough · Canada2024–2025 · 5 papers🎓University at Buffalo, State University of New YorkBuffalo · United States2019–2021 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 workCognitive Neuroscience · 1 workNeuroscience · 1 work

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

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Bias in Large Language Models: Origin, Evaluation, and Mitigation (2024 · 58 citations)
    LLMs now drive much of modern NLP, but they absorb and can amplify social biases. This is risky in high-stakes deployments. Guo et al. address the fragmented state of research on this problem by consolidating it into a single review framework rather than proposing a new model or dataset. They first trace where bias…
  2. Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task (2026 · 2 citations)
    Researchers increasingly consider LLMs as stand-ins for human participants, but it is unclear whether models can reproduce how people differ from one another.

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. Bias in Large Language Models: Origin, Evaluation, and MitigationElectronicsYufei Guo, Muzhe Guo, Juntao Su, et al.2024 · 58 citations
  2. Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency ↗Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) · no summary yetMin Zeng et al.2024 · 14 citations
  3. Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency TaskWorkshop on Cognitive Modeling and Computational Linguistics (CMCL)Mengyang Qiu, Zoe Brisebois, Siena Sun2026 · 2 citations

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

Updated

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