Elamur Research⌕
☰
home / authors / Jiaxin Pei

Jiaxin Pei

Jiaxin Pei has 2 papers in our library, in 2022–2025, most of them on Language models & pretraining; often with David Jurgens, Dirk Hovy, Naitian Zhou.

2 papers in our library · 2022–2025

OpenAlex · ORCID · h-index 10 · 29 works · 346 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 137 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6664

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

🎓Stanford UniversityStanford · United States2023–2026 · 9 paperscurrent🎓University of MichiganAnn Arbor · United States2020–2025 · 16 papers🎓Wuhan UniversityWuhan · China2018–2019 · 2 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.

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksGeneral Social Sciences · 1 workSocial Sciences · 1 work

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

 Works by year

2020s

20251 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. POTATO: The Portable Text Annotation Tool (2022 · 90 citations)
    NLP projects increasingly need annotations for intricate tasks. Existing tools such as Label Studio, Doccano, LightTag and Prodigy each have drawbacks: some depend on external hosting, some offer little layout flexibility for complex designs, and some cost too much for small teams. The authors present POTATO, a free…
  2. Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions (2025 · 22 citations)
    Subjective annotation tasks often show annotator disagreement, and part of it is attributed to sociodemographic background. Zero-shot sociodemographic prompting of LLMs is known to work poorly. This paper asks whether fine-tuning can turn LLMs into accurate demographic models of annotators.

All works

4 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. When “A Helpful Assistant” Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models ↗Findings of the Association for Computational Linguistics: EMNLP 2024 · no summary yetMingqian Zheng et al.2024 · 114 citations
  2. POTATO: The Portable Text Annotation ToolConference on Empirical Methods in Natural Language Processing (EMNLP)Jiaxin Pei, Aparna Ananthasubramaniam, Xingyao Wang, et al.2022 · 90 citations
  3. Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text PerceptionsAnnual Meeting of the Association for Computational Linguistics (ACL)Matthias Orlikowski, Jiaxin Pei, Paul Röttger, et al.2025 · 22 citations
  4. Modeling Information Change in Science Communication with Semantically Matched Paraphrases ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · no summary yetDustin Wright et al.2022 · 17 citations

 Co-authors here

6 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 137 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/jiaxin-pei/