Freda Shi
Impact in
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- Artificial Intelligence in Healthcare and Education
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- Natural Language Processing Techniques
- Topic Modeling
- Semantic Web and Ontologies
- Speech Recognition and Synthesis
- Machine Learning in Healthcare
Papers in
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- Natural Language Processing Techniques 2
- Machine Learning in Healthcare 1
- Topic Modeling 1
- Computational Physics and Python Applications 1
- Text Readability and Simplification 1
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- Multimodal Machine Learning Applications 1
- Co-authors
- Tingchen Fu (1 shared paper)Xinting Huang (1 shared paper)Wei Bi (1 shared paper)Yafu Li (1 shared paper)Longyue Wang (1 shared paper)Yue Zhang (1 shared paper)Leyang Cui (1 shared paper)S. Shi (1 shared paper)
- Journals
- Computational Linguistics (1 paper)Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (1 paper)
- Partner nations
- United StatesTaiwanSingapore
In The Last Decade
Freda Shi
2 papers receiving 29 citations
Freda Shi's Hit Papers
Peers
Comparison fields: 5 of 19
- Health Informatics 3
- Artificial Intelligence 15
- Software 1
- Computer Science Applications 1
- Computer Vision and Pattern Recognition 3
Countries citing papers authored by Freda Shi
This map shows the geographic impact of Freda Shi'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 Freda Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Freda Shi more than expected).
Fields of papers citing papers by Freda Shi
This network shows the impact of papers produced by Freda Shi. 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 Freda Shi. The network helps show where Freda Shi may publish in the future.
Co-authors
The 20 scholars most cited alongside Freda Shi, 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 | 🧜Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models Hit paper breakdown → | 2025 | 27 |
| 2 | 2022 | 4 | |
| 3 | 2023 | 0 |
About Freda Shi
Freda Shi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Infectious Diseases and Organic Chemistry, having authored 3 papers that have together received 31 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (2 papers), Music and Audio Processing (1 paper), Machine Learning in Healthcare (1 paper), Topic Modeling (1 paper), Computational Physics and Python Applications (1 paper), Text Readability and Simplification (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Health Informatics (3 citations), Artificial Intelligence (15 citations), Software (1 citation), Computer Science Applications (1 citation) and Computer Vision and Pattern Recognition (3 citations). Freda Shi has collaborated with scholars based in United States, Taiwan and Singapore. Frequent co-authors include Tingchen Fu, Xinting Huang, Wei Bi, Yafu Li, Longyue Wang, Yue Zhang, Leyang Cui, S. Shi, Yanwen Zhang and Yulong Chen. Their work appears in journals such as Computational Linguistics and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
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.