5 citations · 9 across the 18 of their papers we have counts for
7 papers · 1 filter
A 2-step Framework for Automated Literary Translation Evaluation: Its Promises and Pitfalls
Sheikh Shafayat, Dongkeun Yoon, Woori Jang +3
In this work, we propose and evaluate the feasibility of a two-stage pipeline to evaluate literary machine translation, in a fine-grained manner, from English to Korean. The result…
Uncovering Factor Level Preferences to Improve Human-Model Alignment
Juhyun Oh, Eunsu Kim, Jiseon Kim +4
Large language models (LLMs) often exhibit tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs. While crucia…
BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages
Junho Myung, Nayeon Lee, Yi Zhou +19
Large language models (LLMs) often lack culture-specific knowledge of daily life, especially across diverse regions and non-English languages. Existing benchmarks for evaluating LL…
CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean
Eunsu Kim, Juyoung Suk, Philhoon Oh +3
Despite the rapid development of large language models (LLMs) for the Korean language, there remains an obvious lack of benchmark datasets that test the requisite Korean cultural a…
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…
Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScore
Sheikh Shafayat, Eunsu Kim, Juhyun Oh +1
Evaluating the factuality of long-form large language model (LLM)-generated text is an important challenge. Recently there has been a surge of interest in factuality evaluation for…
Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese
Rifki Afina Putri, Faiz Ghifari Haznitrama, Dea Adhista +1
Large Language Models (LLMs) are increasingly being used to generate synthetic data for training and evaluating models. However, it is unclear whether they can generate a good qual…