---
title: "Who cites Jaewook Kang — 184 papers of our library"
description: "184 papers of the Elamur Research library cite the works of Jaewook Kang, found in their bibliographies; the most cited first."
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---
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# Who cites Jaewook Kang

184 citations of their works from 184 papers of our library (of the 149,439 whose bibliographies we read) · 30 with a page here, the most cited first · [← Jaewook Kang](https://md.research.elamur.ai/free/en/authors/jaewook-kang/)

1. **[Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/) [ACM Transactions on Knowledge Discovery from Data](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-knowledge-discovery-from-data/)**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 814 citations*
2. **[Large Language Models Can Self-Improve](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-can-self/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaxin Huang, Shixiang Gu, Le Hou, et al.*2023 · 575 citations*
3. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
4. [**A Survey on Large Language Models for Code Generation arXiv:2406.00515**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 367 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/)
5. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
6. **[Want To Reduce Labeling Cost? GPT-3 Can Help](https://md.research.elamur.ai/free/en/papers/wang-2021-want-reduce-labeling-cost-gpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shuohang Wang, Yang Liu, Yichong Xu, et al.*2021 · 300 citations*
7. **[A Holistic Approach to Undesired Content Detection in the Real World](https://md.research.elamur.ai/free/en/papers/markov-2022-holistic-approach-undesired-content-detection/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Todor Markov, Chong Zhang, Sandhini Agarwal, et al.*2022 · 263 citations*
8. **[Large Language Models for Data Annotation and Synthesis: A Survey](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Tan, Dawei Li, Song Wang, et al.*2024 · 262 citations*
9. **[Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations](https://md.research.elamur.ai/free/en/papers/li-2023-synthetic-data-generation-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, et al.*2023 · 242 citations*
10. **[On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://md.research.elamur.ai/free/en/papers/long-2024-llms-driven-synthetic-data-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lin Long, Rui Wang, Ruixuan Xiao, et al.*2024 · 237 citations*
11. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding arXiv:2202.04538**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/)
12. **[BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages](https://md.research.elamur.ai/free/en/papers/myung-2024-blend-benchmark-llms-everyday-knowledge/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Junho Myung, Nayeon Lee, Yi Zhou, et al.*2024 · 152 citations*
13. **[Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://md.research.elamur.ai/free/en/papers/chung-2023-increasing-diversity-while-maintaining-accuracy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Chung, Ece Kamar, Saleema Amershi*2023 · 144 citations*
14. **[Data Augmentation for Intent Classification with Off-the-shelf Large Language Models](https://md.research.elamur.ai/free/en/papers/sahu-2022-data-augmentation-intent-classification-off/) [Workshop on Natural Language Processing for Conversational AI](https://md.research.elamur.ai/free/en/venues/nlp4convai/)**Gaurav Sahu, Pau Rodriguez, Issam Laradji, et al.*2022 · 132 citations*
15. **[Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations](https://md.research.elamur.ai/free/en/papers/yoo-2022-ground-truth-labels-matter-deeper/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, et al.*2022 · 114 citations*
16. **[OpenStance: Real-world Zero-shot Stance Detection](https://md.research.elamur.ai/free/en/papers/xu-2022-openstance-real-world-zero-shot/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Hanzi Xu, Slobodan Vucetic, Wenpeng Yin*2022 · 32 citations*
17. [**LLM-as-a-Judge & Reward Model: What They Can and Cannot Do arXiv:2409.11239**Guijin Son, Hyunwoo Ko, Hoyoung Lee, et al.*2024 · 27 citations*](https://md.research.elamur.ai/free/en/papers/son-2024-llm-judge-reward-model-they/)
18. **[Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://md.research.elamur.ai/free/en/papers/eger-2025-transforming-science-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Steffen Eger, Yong Cao, Jennifer D'Souza, et al.*2025 · 25 citations*
19. **[Diversity-oriented Data Augmentation with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-diversity-oriented-data-augmentation-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zaitian Wang, Jinghan Zhang, Xinhao Zhang, et al.*2025 · 17 citations*
20. [**MultiPragEval: Multilingual Pragmatic Evaluation of Large Language Models Proceedings of the 2nd GenBench Workshop on Generalisation (Benchmarking) in NLP**Dojun Park, Jiwoo Lee, Seohyun Park, et al.*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/park-2024-multiprageval-multilingual-pragmatic-evaluation-large/)
21. [**Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges arXiv:2309.12426**Vinay Samuel, Houda Aynaou, Arijit Ghosh Chowdhury, et al.*2023 · 16 citations*](https://md.research.elamur.ai/free/en/papers/samuel-2023-llms-augment-low-resource-reading/)
22. **[A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/anikina-2025-rigorous-evaluation-llm-data-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tatiana Anikina, Jan Cegin, Jakub Simko, et al.*2025 · 11 citations*
23. **[Pragmatic Competence Evaluation of Large Language Models for the Korean Language](https://md.research.elamur.ai/free/en/papers/park-2024-pragmatic-competence-evaluation-large-language/) [Pacific Asia Conference on Language, Information and Computation (PACLIC)](https://md.research.elamur.ai/free/en/venues/paclic/)**Dojun Park, Jiwoo Lee, Hyeyun Jeong, et al.*2024 · 11 citations*
24. [**Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models arXiv:2506.05850**Cheonbok Park; Jeonghoon Kim; Joosung Lee; Sanghwan Bae; Jaegul Choo;, Kang Min Yoo*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/park-2025-cross-lingual-collapse-language-centric/)
25. **[RubricBench: Aligning Model-Generated Rubrics with Human Standards](https://md.research.elamur.ai/free/en/papers/zhou-2026-rubricbench-aligning-model-generated-rubrics/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyi Zhou, Qiyuan Zhang, Yufei Wang, et al.*2026 · 3 citations*
26. **[Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification](https://md.research.elamur.ai/free/en/papers/pecher-2026-better-as-generators-than-classifiers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Branislav Pecher, Jan Cegin, Robert Belanec, et al.*2026 · 1 citation*
27. **[Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages](https://md.research.elamur.ai/free/en/papers/mckenna-2026-synthetic-function-demonstrations-improve-generation/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Nick McKenna, Xinnuo Xu, Jack Williams, et al.*2026 · 1 citation*
28. **[AnalystBench: Benchmarking professional long-form report generation with web-mined multimodal tasks](https://md.research.elamur.ai/free/en/papers/pham-2026-analystbench-benchmarking-professional-long-form/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chau Minh Pham, Zichao Wang, Puneet Mathur, et al.*2026*
29. **[Child-directed speech facilitates production, not comprehension, in BabyLMs](https://md.research.elamur.ai/free/en/papers/bunzeck-2026-child-directed-speech-facilitates-production/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Bastian Bunzeck, Sina Zarrieß*2026*
30. **[Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification](https://md.research.elamur.ai/free/en/papers/merdjanovska-2026-evaluation-pitfalls-sparsity-limitations-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Elena Merdjanovska, Omar Zaidan, Andreas Rücklé*2026*

154 more citing papers of our library have no page here yet.

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