--- title: "Who cites Gangwoo Kim — 99 papers of our library" description: "99 papers of the Elamur Research library cite the works of Gangwoo Kim, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/gangwoo-kim/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/gangwoo-kim/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Gangwoo Kim](https://md.research.elamur.ai/free/en/authors/gangwoo-kim/) / cited by # Who cites Gangwoo Kim 99 citations of their works from 99 papers of our library (of the 141,840 whose bibliographies we read) · 10 with a page here, the most cited first · [← Gangwoo Kim](https://md.research.elamur.ai/free/en/authors/gangwoo-kim/) 1. [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,648 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/) **[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng Chua;, Qing Li*2024 · 1,006 citations* [**AlpaGasus: Training A Better Alpaca with Fewer Data arXiv:2307.08701**Lichang Chen, Shiyang Li, Jun Yan, et al.*2023 · 215 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-alpagasus-training-better-alpaca-fewer/) [**LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods arXiv:2412.05579**Haitao Li, Qian Dong, Junjie Chen, et al.*2024 · 189 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/) [**From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning arXiv:2308.12032**Ming Li, Yong Zhang, Zhitao Li, et al.*2023 · 157 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-quantity-quality-boosting-llm-performance/) [**JudgeLM: Fine-tuned Large Language Models are Scalable Judges arXiv:2310.17631**Lianghui Zhu, Xinggang Wang, Xinlong Wang*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-judgelm-fine-tuned-large-language/) [**From Persona to Personalization: A Survey on Role-Playing Language Agents arXiv:2404.18231**Jiangjie Chen, Xintao Wang, Rui Xu, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-persona-personalization-survey-role-playing/) [**Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning arXiv:2402.00530**Ming Li, Yong Zhang, Shwai He, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-superfiltering-weak-strong-data-filtering/) [**HeQ: a Large and Diverse Hebrew Reading Comprehension Benchmark**Amir DN Cohen, Hilla Merhav, Yoav Goldberg, et al.*2023 · 13 citations*](https://md.research.elamur.ai/free/en/papers/cohen-2023-heq-large-diverse-hebrew-reading/) **[A Binary Problem in Binary QA: Diverse LLMs or Diverse Question Interpretations? That Is the Ensembling Question](https://md.research.elamur.ai/free/en/papers/rosales-2026-binary-problem-binary-qa-diverse/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Rafael Rosales, Santiago Miret*2026* 89 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/gangwoo-kim/) — Gangwoo Kim