11 citations · 11 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs
Mahjabin Nahar, Nafis Irtiza Tripto, Aiping Xiong +2
As AI-generated and AI-assisted content floods online spaces, source labels attached to such content can distort human reasoning judgments, with downstream consequences for moderat…
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…