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Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations
This research aims to understand the human perception of LLM hallucinations by systematically varying the degree of hallucination (genuine, minor hallucination, major hallucination) and examining its interaction with warning (i. e., a warning of potential inaccuracies: absent vs. present).
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration
The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising.
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