Tanmoy Chakraborty, Iiit Delhi, Alexander Fraser, Viktor Hangya, Sarah Masud, Sahajpreet Singh, Lmu
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For subjective tasks such as hate detection, where people perceive hate differently, the Large Language Model's (LLM) ability to represent diverse groups is unclear. By including additional context in prompts, we comprehensively analyze LLM's sensitivity to geographical priming, persona attributes, and numerical information to assess how well the needs of various groups are reflected. Our findings on two LLMs, five languages, and six datasets reveal that mimicking persona-based attributes leads to annotation variability. Meanwhile, incorporating geographical signals leads to better regional alignment. We also find that the LLMs are sensitive to numerical anchors, indicating the ability to leverage community-based flagging efforts and exposure to adversaries. Our work provides preliminary guidelines and highlights the nuances of applying LLMs in culturally sensitive cases. 1 * Equal Contribution 1 Disclaimer: The paper contains examples of strong and hateful language. I like my girlfriends like I like my dogs Rescued from a young age and stays in their cage. "Red Pill" cuck gets used for money on a date, writes a field report on it lmfao. This is how they work. They are domestic terrorists. They are taking over corporations world wide and nothing good will come of it.
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