77 citations · 83 across the 4 of their papers we have counts for
4 papers
The Generative AI Paradox on Evaluation: What It Can Solve, It May Not Evaluate
Juhyun Oh, Eunsu Kim, Inha Cha +1
This paper explores the assumption that Large Language Models (LLMs) skilled in generation tasks are equally adept as evaluators. We assess the performance of three LLMs and one op…
The Grind for Good Data: Understanding ML Practitioners' Struggles and Aspirations in Making Good Data
Inha Cha, Juhyun Oh, Cheul Young Park +2
We thought data to be simply given, but reality tells otherwise; it is costly, situation-dependent, and muddled with dilemmas, constantly requiring human intervention. The ML commu…
KOLD: Korean Offensive Language Dataset
Younghoon Jeong, Juhyun Oh, Jaimeen Ahn +4
Recent directions for offensive language detection are hierarchical modeling, identifying the type and the target of offensive language, and interpretability with offensive span an…
KLUE: Korean Language Understanding Evaluation
Sungjoon Park, Jihyung Moon, Sungdong Kim +28
We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification,…