Yuling Gu

503 citations
13 papers · 249 · 1 hit paper · h-index 4

Impact in

Papers in

Yuling Gu

10 papers receiving 244 citations

Yuling Gu's Hit Papers

Can AI language models replace human participants? 2023 · 210 citations
2100+1+2Years since publication50100150200

Peers

Yuling Gu
Comparison fields: 5 of 78
  • Health Informatics 18
  • General Social Sciences 31
  • General Decision Sciences 9
  • Artificial Intelligence 105
  • Applied Psychology 12
Replace Marcel Binz with:
Marcel Binz Germany
Simone Conia Italy
Sunipa Dev United States
Paul Röttger United Kingdom
Maria Antoniak United States
Kellie Webster United States
Erica J. Yoon United States
Kevin R. McKee United Kingdom
Omar Ahmed Shaikh Pakistan
Michael Pin-Chuan Lin Canada
Yuling Gu relative to Marcel Binz Germany Marcel Binz's profile →
Citations per field
00.5×1.7×
Marcel Binz · 1×
Citations per year

Countries citing papers authored by Yuling Gu

Since Specialization
Citations

This map shows the geographic impact of Yuling Gu'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 Yuling Gu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuling Gu more than expected).

Fields of papers citing papers by Yuling Gu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuling Gu. 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 Yuling Gu. The network helps show where Yuling Gu may publish in the future.

Co-authors

The 25 scholars most cited alongside Yuling Gu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yuling Gu Line = papers co-authored together Yuling Gu links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1
Can AI language models replace human participants?
Hit paper breakdown →
2023210
2 202311
3 202211
4 20197
5 20223
6 20232
7 20232
8 20251
9 20161
10 20201
11 20240
12 20210
13 20240

About Yuling Gu

Yuling Gu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Linguistics and Language, Surgery and Molecular Biology, having authored 13 papers that have together received 249 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Natural Language Processing Techniques (5 papers), Explainable Artificial Intelligence (XAI) (2 papers), Multimodal Machine Learning Applications (2 papers), Peroxisome Proliferator-Activated Receptors (1 paper), Linguistic Variation and Morphology (1 paper), Eicosanoids and Hypertension Pharmacology (1 paper) and AI-based Problem Solving and Planning (1 paper). The work is most often cited by research in Health Informatics (18 citations), General Social Sciences (31 citations), General Decision Sciences (9 citations), Artificial Intelligence (105 citations) and Applied Psychology (12 citations). Yuling Gu has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Niket Tandon, Kurt Gray, Peter Clark, Bhavana Dalvi, Jinjiang Pang, Edward A. Fisher, Junbo Ge, Nancy F. Chen, Hao Ding and Yujia Liu. Their work appears in journals such as Arteriosclerosis Thrombosis and Vascular Biology, Trends in Cognitive Sciences, BioMed Research International, Taiwanese Journal of Obstetrics and Gynecology and Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

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.

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