--- title: "Lijun Mei — author profile and papers — Elamur Research" description: "Lijun Mei, China Mobile: 2 papers on Elamur Research (2026), h-index 0 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/lijun-mei/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/lijun-mei/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Lijun Mei # Lijun Mei Lijun Mei has 2 papers in our library, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Junlan Feng](https://md.research.elamur.ai/free/en/authors/junlan-feng/), [Qian Hu](https://md.research.elamur.ai/free/en/authors/qian-hu/), [Xue Han](https://md.research.elamur.ai/free/en/authors/xue-han/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5139847092) · [**h-index 0**](https://openalex.org/authors/A5139847092) · [5 works](https://openalex.org/works?filter=author.id:A5139847092) · [0 citations](https://openalex.org/works?filter=author.id:A5139847092&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/lijun-mei/#papers) · [⭐ Paper Stars ranking: #12098](https://md.research.elamur.ai/free/en/paper-stars/page/121/#star-12098) 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=Lijun+Mei) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Lijun+Mei) · [dblp](https://dblp.org/search?q=Lijun+Mei) · [ACL Anthology](https://aclanthology.org/search/?q=Lijun+Mei) ## Affiliations [🏢**China Mobile** Beijing · China 2026 · 3 papers*current*](https://md.research.elamur.ai/free/en/companies/china-mobile/) 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 4 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 2 Research subjects and languages the work is about.[Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/lijun-mei/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[ChildEval:WHEN LARGE LANGUAGE MODELS MEET CHILDREN'S PERSONALITIES](https://md.research.elamur.ai/free/en/papers/luo-2026-childeval-when-large-language-models/)** *(2026)* LLM-based chatbots are increasingly used as companions for young children. Yet nobody has systematically tested whether these models pick up on and respect a child's preferences. Existing personalization benchmarks mostly target adults, and child-focused ones cover only narrow aspects such as safety or style… 2. **[Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-soft-orthogonal-low-rank-adaptation/)** *(2026)* The paper addresses continual learning (CL) for large language models. Full retraining is too costly at this scale, and most existing CL methods focus only on preventing catastrophic forgetting, without exploiting knowledge that tasks could share. The authors first observe that better-performing CL models produce… ## Co-authors here 5 co-authors with a page, the most papers together first. - [Junlan Feng](https://md.research.elamur.ai/free/en/authors/junlan-feng/) ×2 - [Qian Hu](https://md.research.elamur.ai/free/en/authors/qian-hu/) ×2 - [Xue Han](https://md.research.elamur.ai/free/en/authors/xue-han/) ×2 - [Yitong Wang](https://md.research.elamur.ai/free/en/authors/yitong-wang/) - [Ziqing Wang](https://md.research.elamur.ai/free/en/authors/ziqing-wang/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [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/) - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) Updated 1 November 2026