  Dehumanization, characterized as a subtle yet harmful manifestation of hate
speech, involves denying individuals of their human qualities and often results
in violence against marginalized groups. Despite significant progress in
Natural Language Processing across various domains, its application in
detecting dehumanizing language is limited, largely due to the scarcity of
publicly available annotated data for this domain. This paper evaluates the
performance of cutting-edge NLP models, including GPT-4, GPT-3.5, and LLAMA-2,
in identifying dehumanizing language. Our findings reveal that while these
models demonstrate potential, achieving a 70\% accuracy rate in distinguishing
dehumanizing language from broader hate speech, they also display biases. They
are over-sensitive in classifying other forms of hate speech as dehumanization
for a specific subset of target groups, while more frequently failing to
identify clear cases of dehumanization for other target groups. Moreover,
leveraging one of the best-performing models, we automatically annotated a
larger dataset for training more accessible models. However, our findings
indicate that these models currently do not meet the high-quality data
generation threshold necessary for this task.
