1 citations · 4 across the 18 of their papers we have counts for
8 papers · 1 filter
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling
Daehoon Gwak, Junwoo Park, Minho Park +4
Predicting future international events from textual information, such as news articles, has tremendous potential for applications in global policy, strategic decision-making, and g…
Breaking Chains: Unraveling the Links in Multi-Hop Knowledge Unlearning
Minseok Choi, ChaeHun Park, Dohyun Lee +1
Large language models (LLMs) serve as giant information stores, often including personal or copyrighted data, and retraining them from scratch is not a viable option. This has led…
Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts
ChaeHun Park, Hojun Cho, Jaegul Choo
This paper explores integrating Automatic Speech Recognition (ASR) into natural language query systems to improve weather forecasting efficiency for Korean meteorologists. We addre…
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering
ChaeHun Park, Koanho Lee, Hyesu Lim +5
Building a reliable visual question answering~(VQA) system across different languages is a challenging problem, primarily due to the lack of abundant samples for training. To addre…
Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration
ChaeHun Park, Yujin Baek, Jaeseok Kim +3
To create culturally inclusive vision-language models (VLMs), developing a benchmark that tests their ability to address culturally relevant questions is essential. Existing approa…
The Comparative Trap: Pairwise Comparisons Amplifies Biased Preferences of LLM Evaluators
Hawon Jeong, ChaeHun Park, Jimin Hong +2
As large language models (LLMs) are increasingly used as evaluators for natural language generation tasks, ensuring unbiased assessments is essential. However, LLM evaluators often…
Can Tool-augmented Large Language Models be Aware of Incomplete Conditions?
Seungbin Yang, ChaeHun Park, Taehee Kim +1
Recent advancements in integrating large language models (LLMs) with tools have allowed the models to interact with real-world environments. However, these tool-augmented LLMs ofte…
PairEval: Open-domain Dialogue Evaluation with Pairwise Comparison
ChaeHun Park, Minseok Choi, Dohyun Lee +1
Building a reliable and automated evaluation metric is a necessary but challenging problem for open-domain dialogue systems. Recent studies proposed evaluation metrics that assess…