---
title: "Seungone Kim — papers of 2023 — 4 papers, the most cited first"
description: "Seungone Kim — papers of 2023: 4 papers on Elamur Research, the most cited first — what each work is about in our own words."
canonical: "https://research.elamur.ai/free/en/authors/seungone-kim/year/2023/"
---
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# Seungone Kim — papers of 2023 — 4 papers, the most cited first

4 papers · [all years of Seungone Kim](https://md.research.elamur.ai/free/en/authors/seungone-kim/#years)

- [Prometheus: Inducing Fine-grained Evaluation Capability in Language Models arXiv:2310.08491](https://md.research.elamur.ai/free/en/papers/kim-2023-prometheus-inducing-fine-grained-evaluation/) — Seungone Kim, Jamin Shin, Yejin Cho, et al. · 2023 · 281 citations

**[The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning](https://md.research.elamur.ai/free/en/papers/kim-2023-cot-collection-improving-zero-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Seungone Kim, Se Joo, Doyoung Kim, et al.*2023 · 118 citations*

- [FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets arXiv:2307.10928](https://md.research.elamur.ai/free/en/papers/ye-2023-flask-fine-grained-language-model/) — Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al. · 2023 · 100 citations
- [Exploring the Benefits of Training Expert Language Models over Instruction Tuning arXiv:2302.03202](https://md.research.elamur.ai/free/en/papers/jang-2023-exploring-benefits-training-expert-language/) — Joel Jang, Seungone Kim, Seonghyeon Ye, et al. · 2023 · 63 citations