--- title: "Ning Cheng — author profile and papers — Elamur Research" description: "Ning Cheng, Ping An: 2 papers on Elamur Research (2023–2024), h-index 5 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/ning-cheng/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/ning-cheng/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Ning Cheng # Ning Cheng Ning Cheng has 2 papers in our library, in 2023–2024, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/); often with [Jianzong Wang](https://md.research.elamur.ai/free/en/authors/jianzong-wang/), [Ming Li](https://md.research.elamur.ai/free/en/authors/ming-li/), [Tianyi Zhou](https://md.research.elamur.ai/free/en/authors/tianyi-zhou/). 2 papers in our library · 2023–2024 [OpenAlex](https://openalex.org/A5065576761) · [ORCID](https://orcid.org/0000-0003-0909-5328) · [**h-index 5**](https://openalex.org/authors/A5065576761) · [25 works](https://openalex.org/works?filter=author.id:A5065576761) · [95 citations](https://openalex.org/works?filter=author.id:A5065576761&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/ning-cheng/#papers) · [🔗 Cited 206 times in our library](https://md.research.elamur.ai/free/en/authors/ning-cheng/cited-by/) · [⭐ Paper Stars ranking: #4581](https://md.research.elamur.ai/free/en/paper-stars/page/46/#star-4581) 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=Ning+Cheng) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Ning+Cheng) · [dblp](https://dblp.org/search?q=Ning+Cheng) · [ACL Anthology](https://aclanthology.org/search/?q=Ning+Cheng) ## Affiliations [🏢**Ping An** Shenzhen · China 2022–2025 · 22 papers*current*](https://md.research.elamur.ai/free/en/companies/ping-an/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Concepts, shelves and topics Of its works in our library: the number of works on each and its rating among them. 📚 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 1 Research subjects and languages the work is about.[Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) ## Works by year ### 2020s [**2024** 1 paper Ning Cheng's papers of 2024](https://md.research.elamur.ai/free/en/papers/li-2024-superfiltering-weak-strong-data-filtering/) · [**2023** 1 paper Ning Cheng's papers of 2023](https://md.research.elamur.ai/free/en/papers/li-2023-quantity-quality-boosting-llm-performance/) ## Papers The most cited first (OpenAlex and our library). 1. **[From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning](https://md.research.elamur.ai/free/en/papers/li-2023-quantity-quality-boosting-llm-performance/)** *(2023 · 157 citations)* LIMA showed that instruction tuning depends more on data quality than on volume. Picking good samples out of large open instruction sets still usually takes human curation or an external judge such as ChatGPT. This paper lets the target model choose its own training data. First, a base LLM gets a short warm-up… 2. **[Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning](https://md.research.elamur.ai/free/en/papers/li-2024-superfiltering-weak-strong-data-filtering/)** *(2024 · 60 citations)* Selecting a small, high-quality subset of instruction data can make LLM instruction tuning cheaper and better. ## Co-authors here 6 co-authors with a page, the most papers together first. - [Jianzong Wang](https://md.research.elamur.ai/free/en/authors/jianzong-wang/) ×2 - [Ming Li](https://md.research.elamur.ai/free/en/authors/ming-li/) ×2 - [Tianyi Zhou](https://md.research.elamur.ai/free/en/authors/tianyi-zhou/) ×2 - [Yong Zhang](https://md.research.elamur.ai/free/en/authors/yong-zhang/) ×2 - [Zhitao Li](https://md.research.elamur.ai/free/en/authors/zhitao-li/) ×2 - [Lichang Chen](https://md.research.elamur.ai/free/en/authors/lichang-chen/) ## Cited by Papers of our library cite their works 206 times (of the 141,840 papers whose bibliographies we read); [10 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/ning-cheng/cited-by/). Updated 24 October 2026