Nayeon Lee

1.0k total citations · 1 hit paper
6 papers, 417 citations indexed

About

Nayeon Lee is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Nayeon Lee has authored 6 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 2 papers in Information Systems and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Nayeon Lee's work include Multimodal Machine Learning Applications (2 papers), Natural Language Processing Techniques (2 papers) and Topic Modeling (2 papers). Nayeon Lee is often cited by papers focused on Multimodal Machine Learning Applications (2 papers), Natural Language Processing Techniques (2 papers) and Topic Modeling (2 papers). Nayeon Lee collaborates with scholars based in South Korea and Hong Kong. Nayeon Lee's co-authors include Bryan Wilie, Pascale Fung, Ziwei Ji, Tiezheng Yu, Willy Chung, Yan Xu, Yejin Bang, Dan Su, Wenliang Dai and Samuel Cahyawijaya and has published in prestigious journals such as Transactions of the Association for Computational Linguistics, Rare & Special e-Zone (The Hong Kong University of Science and Technology) and Journal of KIISE.

In The Last Decade

Nayeon Lee

6 papers receiving 402 citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

2023 388 citations Yejin Bang, Samuel Cahyawijaya et al. Rare & Special e-Zone (The Hong Kong University of Science and Technology) profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Nayeon Lee	280	79	61	32	28	6	417
Willy Chung	262 0.9×	79 1.0×	58 1.0×	30 0.9×	26 0.9×	5	396
Holy Lovenia	286 1.0×	79 1.0×	60 1.0×	31 1.0×	27 1.0×	13	431
Yejin Bang	291 1.0×	80 1.0×	59 1.0×	39 1.2×	26 0.9×	11	468
Bryan Wilie	361 1.3×	79 1.0×	82 1.3×	33 1.0×	27 1.0×	14	509
Arkadiusz Janz	297 1.1×	119 1.5×	36 0.6×	36 1.1×	31 1.1×	19	465
Kamil Kanclerz	359 1.3×	123 1.6×	41 0.7×	46 1.4×	31 1.1×	11	529
Julita Bielaniewicz	295 1.1×	124 1.6×	36 0.6×	36 1.1×	31 1.1×	8	458
Bartłomiej Koptyra	246 0.9×	118 1.5×	32 0.5×	30 0.9×	31 1.1×	5	403
Konrad Wojtasik	252 0.9×	118 1.5×	31 0.5×	30 0.9×	31 1.1×	3	442
Łukasz Radliński	243 0.9×	118 1.5×	31 0.5×	30 0.9×	31 1.1×	2	397

Countries citing papers authored by Nayeon Lee

Since Specialization

Citations

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

Fields of papers citing papers by Nayeon Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nayeon Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Nayeon Lee. A scholar is included among the top collaborators of Nayeon Lee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nayeon Lee. Nayeon Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

Kim, Jiseon, et al.. (2024). KoBBQ: Korean Bias Benchmark for Question Answering. Transactions of the Association for Computational Linguistics. 12. 507–524. 3 indexed citations

Lee, Nayeon, et al.. (2023). Hate Speech Classifiers are Culturally Insensitive. 35–46. 8 indexed citations

Ji, Ziwei, Zihan Liu, Nayeon Lee, et al.. (2023). RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 4504–4522. 16 indexed citations

Bang, Yejin, Samuel Cahyawijaya, Nayeon Lee, et al.. (2023). A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 675–718. 388 indexed citations breakdown →

Kim, Dong-Geun, et al.. (2022). A Graph2Tree Model for Solving Korean Math Word Problems. Journal of KIISE. 49(10). 807–815. 1 indexed citations

Lee, Nayeon, et al.. (2015). A Study on Exploring Direction to Improve Local Newspaper throughout the Usage and Evaluation of Local Newspaper by College Students : Focusing on written interview on college students in Busan. Journal of Communication Science. 15(4). 307–307. 1 indexed citations

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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