11 citations · 11 across the 4 of their papers we have counts for
5 papers
When AI Says "I Am Unable to Answer": Understanding User Responses to AI Refusals
Mahjabin Nahar, Eun-Ju Lee, Yujin Heo +1
While refusal-based safeguards to mitigate hallucinations in large language models (LLMs) are becoming increasingly common, they may conflict with users' preferences for definitive…
TriBench-Ko: Evaluating LLM Risks in Judicial Workflows
Haesung Lee, Gyubin Choi, Eun-Ju Lee +5
Large language models (LLMs) are increasingly integrated into legal workflows. However, existing benchmarks primarily address proxy tasks, such as bar examination performance or cl…
Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks
JuneHyoung Kwon, MiHyeon Kim, Eunju Lee +3
While Large Vision-Language Models (LVLMs) offer powerful capabilities, they pose privacy risks by unintentionally memorizing sensitive personal information. Current unlearning ben…
Catch Me if You Search: When Contextual Web Search Results Affect the Detection of Hallucinations
Mahjabin Nahar, Eun-Ju Lee, Jin Won Park +1
While we increasingly rely on large language models (LLMs) for various tasks, these models are known to produce inaccurate content or 'hallucinations' with potentially disastrous c…
Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations
Mahjabin Nahar, Haeseung Seo, Eun-Ju Lee +2
The widespread adoption and transformative effects of large language models (LLMs) have sparked concerns regarding their capacity to produce inaccurate and fictitious content, refe…