1 citations · 1 across the 6 of their papers we have counts for
6 papers
Korean Canonical Legal Benchmark: Toward Knowledge-Independent Evaluation of LLMs' Legal Reasoning Capabilities
Hongseok Oh, Wonseok Hwang, Kyoung-Woon On
We introduce the Korean Canonical Legal Benchmark (KCL), a benchmark designed to assess language models' legal reasoning capabilities independently of domain-specific knowledge. KC…
LRAGE: Legal Retrieval Augmented Generation Evaluation Tool
Minhu Park, Hongseok Oh, Eunkyung Choi +1
Recently, building retrieval-augmented generation (RAG) systems to enhance the capability of large language models (LLMs) has become a common practice. Especially in the legal doma…
Taxation Perspectives from Large Language Models: A Case Study on Additional Tax Penalties
Eunkyung Choi, Youngjin Suh, Siun Lee +5
How capable are large language models (LLMs) in the domain of taxation? Although numerous studies have explored the legal domain, research dedicated to taxation remains scarce. Mor…
Do Vision Encoders Truly Explain Object Hallucination?: Mitigating Object Hallucination via Simple Fine-Grained CLIPScore
Hongseok Oh, Wonseok Hwang
Recently, Large Vision-Language Models (LVLMs) show remarkable performance across various domains. However, these models suffer from object hallucination. In this work, we study ob…
Does Alignment Tuning Really Break LLMs' Internal Confidence?
Hongseok Oh, Wonseok Hwang
Large Language Models (LLMs) have shown remarkable progress, but their real-world application necessitates reliable calibration. This study conducts a comprehensive analysis of cal…
On the Consideration of AI Openness: Can Good Intent Be Abused?
Yeeun Kim, Hyunseo Shin, Eunkyung Choi +3
Open source is a driving force behind scientific advancement.However, this openness is also a double-edged sword, with the inherent risk that innovative technologies can be misused…