Niklas Muennighoff
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education 1
- Artificial Intelligence top 5%
- Natural Language Processing Techniques 6
- Topic Modeling 5
- Advanced Graph Neural Networks 1
- Explainable Artificial Intelligence (XAI) 1
- Speech Recognition and Synthesis 1
- General Social Sciences top 10%
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- Ethics and Social Impacts of AI 1
- Co-authors
- Loïc MagneNils ReimersZheng YongLintang SutawikaStella BidermanAlham Fikri AjiThomas J. WangColin Raffel
- Journals
- Nature Machine Intelligence (1 paper)Rare & Special e-Zone (The Hong Kong University of Science and Technology) (1 paper)
- Partner nations
- United StatesUnited KingdomUnited Arab Emirates
In The Last Decade
Niklas Muennighoff
8 papers receiving 357 citations
Hit Papers
Peers
Comparison fields: 5 of 77
- Health Informatics 15
- Artificial Intelligence 302
- Computer Vision and Pattern Recognition 55
- General Social Sciences 7
- Information Systems 37
Countries citing papers authored by Niklas Muennighoff
This map shows the geographic impact of Niklas Muennighoff's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Niklas Muennighoff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Niklas Muennighoff more than expected).
Fields of papers citing papers by Niklas Muennighoff
This network shows the impact of papers produced by Niklas Muennighoff. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Niklas Muennighoff. The network helps show where Niklas Muennighoff may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Niklas Muennighoff, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 5 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 18 | |
| 4 | 2024 | 17 | |
| 5 | MTEB: Massive Text Embedding Benchmarkbreakdown → | 2023 | 121 |
| 6 | 2023 | 9 | |
| 7 | Crosslingual Generalization through Multitask Finetuningbreakdown → | 2023 | 190 |
| 8 | 2022 | 18 |
About Niklas Muennighoff
Niklas Muennighoff is a scholar working on Health Informatics, Artificial Intelligence, Safety Research, Infectious Diseases and Organic Chemistry, having authored 8 papers that have together received 379 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (6 papers), Topic Modeling (5 papers), Advanced Graph Neural Networks (1 paper), Explainable Artificial Intelligence (XAI) (1 paper), Artificial Intelligence in Healthcare and Education (1 paper), Speech Recognition and Synthesis (1 paper) and Ethics and Social Impacts of AI (1 paper). The work is most often cited by research in Health Informatics (15 citations), Artificial Intelligence (302 citations), Computer Vision and Pattern Recognition (55 citations), General Social Sciences (7 citations) and Information Systems (37 citations). Niklas Muennighoff has collaborated with scholars based in United States, United Kingdom and United Arab Emirates. Frequent co-authors include Loïc Magne, Nils Reimers, Zheng Yong, Lintang Sutawika, Stella Biderman, Alham Fikri Aji, Thomas J. Wang, Colin Raffel, Edward Raff and Khalid Almubarak. Their work appears in journals such as Nature Machine Intelligence and Rare & Special e-Zone (The Hong Kong University of Science and Technology).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.