Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection

Understand

The fairness and trustworthiness of Large Language Models (LLMs) are receiving increasing attention.

• Implicit hate speech, which employs indirect language to convey hateful intentions, occupies a significant portion of practice.
• However, the extent to which LLMs effectively address this issue remains insufficiently examined.
• This paper delves into the capability of LLMs to detect implicit hate speech (Classification Task) and express confidence in their responses (Calibration Task).