---
title: "Peiquan Jin — author profile and papers — Elamur Research"
description: "Peiquan Jin, University of Science and Technology of China: 3 papers on Elamur Research (2026), h-index 21 — affiliations, topics…"
canonical: "https://research.elamur.ai/free/en/authors/peiquan-jin/"
---
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# Peiquan Jin

Peiquan Jin has 3 papers in our library, in 2026, most of them on [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/), [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Li Ni](https://md.research.elamur.ai/free/en/authors/li-ni/), [Lin Mu](https://md.research.elamur.ai/free/en/authors/lin-mu/), [Yiwen Zhang](https://md.research.elamur.ai/free/en/authors/yiwen-zhang/).

3 papers in our library · 2026

[OpenAlex](https://openalex.org/A5087025546) · [ORCID](https://orcid.org/0000-0002-3871-0548) · [Wikidata](https://www.wikidata.org/wiki/Q130822277) · [**h-index 21**](https://openalex.org/authors/A5087025546) · [324 works](https://openalex.org/works?filter=author.id:A5087025546) · [2,082 citations](https://openalex.org/works?filter=author.id:A5087025546&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/peiquan-jin/#papers) · [⭐ Paper Stars ranking: #11392](https://md.research.elamur.ai/free/en/paper-stars/page/114/#star-11392)

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Peiquan+Jin) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Peiquan+Jin) · [dblp](https://dblp.org/search?q=Peiquan+Jin) · [ACL Anthology](https://aclanthology.org/search/?q=Peiquan+Jin)

## Affiliations

[🎓**University of Science and Technology of China** Hefei · China 2003–2026 · 288 papersEmployment*current*](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) · [🏛️**Chinese Academy of Sciences** Beijing · China 2013–2024 · 62 papers](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/)

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 5 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/)

🧭 Topics 2 Research subjects and languages the work is about.[Efficient models & compression · 3 works](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/)

## Works by year

### 2020s

[**2026** 3 papers](https://md.research.elamur.ai/free/en/authors/peiquan-jin/year/2026/)

## Papers

The most cited first (OpenAlex and our library).

1. **[Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions](https://md.research.elamur.ai/free/en/papers/zhang-2026-polynomial-expansion-rank-adaptation-enhancing/)** *(2026 · 1 citation)* LoRA, the standard parameter-efficient method for fine-tuning LLMs, builds each weight update as a product of two low-rank matrices.
2. **[GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation](https://md.research.elamur.ai/free/en/papers/mu-2026-graphlora-structure-aware-low-rank/)** *(2026)* LLM-based recommenders have trouble combining a language model's textual knowledge with collaborative signals from user–item interactions, which are naturally graph-shaped.
3. **[TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models](https://md.research.elamur.ai/free/en/papers/mu-2026-talklora-communication-aware-mixture-low/)** *(2026)* Mixture-of-Experts variants of LoRA (MoELoRA) split adaptation across several low-rank experts, but each expert works in isolation.

## Co-authors here

5 co-authors with a page, the most papers together first.

- [Li Ni](https://md.research.elamur.ai/free/en/authors/li-ni/) ×3
- [Lin Mu](https://md.research.elamur.ai/free/en/authors/lin-mu/) ×3
- [Yiwen Zhang](https://md.research.elamur.ai/free/en/authors/yiwen-zhang/) ×3
- [Lei Sang](https://md.research.elamur.ai/free/en/authors/lei-sang/) ×2
- [Zhize Wu](https://md.research.elamur.ai/free/en/authors/zhize-wu/) ×2

## Venues

Where their works here were published, the most works first, with the editions of those that have a page.

- [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) ×2: [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ×2
- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)

Updated 1 November 2026
