Elamur Research⌕
☰
home / authors / Kai Lv

Kai Lv

Kai Lv has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Information retrieval & search; often with Xipeng Qiu, Hang Yan, Qipeng Guo.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 12 · 35 works · 379 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 145 times in our library · ⭐ Paper Stars ranking: #4251

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

🎓Peking UniversityBeijing · China2023–2026 · 11 paperscurrent🔬Laoshan LaboratoryQingdao2023–2026 · 6 paperscurrent🎓Shandong UniversityJinan · China2011–2023 · 10 papers🔬State Key Laboratory of Turbulence and Complex SystemsBeijing2023–2024 · 6 papers🎓Fudan UniversityShanghai · China2023–2025 · 4 papers🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2024–2025 · 3 papers🎓Jilin Normal UniversitySiping · China2010–2012 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksInformation retrieval & search · 1 work

 Works by year

2020s

20241 paperKai Lv's papers of 202420231 paperKai Lv's papers of 2023

 Papers

The most cited first (OpenAlex and our library).

  1. Unified Demonstration Retriever for In-Context Learning (2023 · 144 citations)
    In-context learning (ICL) lets a language model solve a task from a few input-output examples placed in its prompt, but its accuracy depends heavily on which examples are chosen.
  2. Full Parameter Fine-tuning for Large Language Models with Limited Resources (2024 · 143 citations)
    Full parameter fine-tuning of large language models usually requires expensive hardware such as multiple 80GB GPUs.

 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 145 times (of the 141,840 papers whose bibliographies we read); 12 citing papers with a page here →.

Updated

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