--- title: "Wenyi Li — author profile and papers — Elamur Research" description: "Wenyi Li, University of Electronic Science and Technology of China: 2 papers on Elamur Research (2026), h-index 1 — affiliations, topics…" canonical: "https://research.elamur.ai/free/en/authors/wenyi-li/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/wenyi-li/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Wenyi Li # Wenyi Li Wenyi Li 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 [Jie Ou](https://md.research.elamur.ai/free/en/authors/jie-ou/), [Jinyu Guo](https://md.research.elamur.ai/free/en/authors/jinyu-guo/), [Wenhong Tian](https://md.research.elamur.ai/free/en/authors/wenhong-tian/). 2 papers in our library · 2026 [OpenAlex](https://openalex.org/A5139829991) · [**h-index 1**](https://openalex.org/authors/A5139829991) · [8 works](https://openalex.org/works?filter=author.id:A5139829991) · [11 citations](https://openalex.org/works?filter=author.id:A5139829991&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/wenyi-li/#papers) · [⭐ Paper Stars ranking: #13991](https://md.research.elamur.ai/free/en/paper-stars/page/140/#star-13991) 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=Wenyi+Li) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Wenyi+Li) · [dblp](https://dblp.org/search?q=Wenyi+Li) · [ACL Anthology](https://aclanthology.org/search/?q=Wenyi+Li) ## Affiliations [🎓**University of Electronic Science and Technology of China** Chengdu · China 2026 · 7 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-electronic-science-and-technology-of-china/) 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 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/wenyi-li/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[AdapShot: Adaptive Many-Shot In-Context Learning with Semantic-Aware KV Cache Reuse](https://md.research.elamur.ai/free/en/papers/ou-2026-adapshot-adaptive-many-shot-context/)** *(2026)* Many-shot in-context learning (ICL) puts hundreds or thousands of demonstrations into an LLM prompt. Current methods use one fixed shot count for every query. The authors show this is a poor fit: accuracy does not rise steadily with more examples, and the best count depends on the model, the task and the individual… 2. **[CAP: Controllable Alignment Prompting for Unlearning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-cap-controllable-alignment-prompting-unlearning/)** *(2026)* LLMs trained on uncurated data can memorize sensitive or hazardous facts. Most unlearning techniques remove such knowledge by editing weights, which is costly, can damage general ability, and does not work for closed-source models. CAP takes a different route: the target LLM stays frozen, and a small language model… ## Co-authors here 5 co-authors with a page, the most papers together first. - [Jie Ou](https://md.research.elamur.ai/free/en/authors/jie-ou/) ×2 - [Jinyu Guo](https://md.research.elamur.ai/free/en/authors/jinyu-guo/) ×2 - [Wenhong Tian](https://md.research.elamur.ai/free/en/authors/wenhong-tian/) ×2 - [Zhaokun Wang](https://md.research.elamur.ai/free/en/authors/zhaokun-wang/) ×2 - [Jingwen Pu](https://md.research.elamur.ai/free/en/authors/jingwen-pu/) ## 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/) ×2: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×2 Updated 1 November 2026