--- title: "Li Ni — author profile and papers · Efficient models & compression" description: "Li Ni, Anhui University: 3 papers on Elamur Research (2026), h-index 1 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/li-ni/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/li-ni/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Li Ni # Li Ni Li Ni 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 [Lin Mu](https://md.research.elamur.ai/free/en/authors/lin-mu/), [Peiquan Jin](https://md.research.elamur.ai/free/en/authors/peiquan-jin/), [Yiwen Zhang](https://md.research.elamur.ai/free/en/authors/yiwen-zhang/). 3 papers in our library · 2026 [OpenAlex](https://openalex.org/A5139841948) · [**h-index 1**](https://openalex.org/authors/A5139841948) · [5 works](https://openalex.org/works?filter=author.id:A5139841948) · [1 citations](https://openalex.org/works?filter=author.id:A5139841948&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/li-ni/#papers) · [⭐ Paper Stars ranking: #11383](https://md.research.elamur.ai/free/en/paper-stars/page/114/#star-11383) 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=Li+Ni) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Li+Ni) · [dblp](https://dblp.org/search?q=Li+Ni) · [ACL Anthology](https://aclanthology.org/search/?q=Li+Ni) ## Affiliations [🎓**Anhui University** Hefei · China 2026 · 5 papers*current*](https://md.research.elamur.ai/free/en/universities/anhui-university/) 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 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/li-ni/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. - [Lin Mu](https://md.research.elamur.ai/free/en/authors/lin-mu/) ×3 - [Peiquan Jin](https://md.research.elamur.ai/free/en/authors/peiquan-jin/) ×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