This paper explores the challenges of detecting LGBTQIA+ hate speech of large language models across multiple languages, including English, Italian, Chinese and (code-switched) English-Tamil, examining the impact of machine translation and whether the nuances of hate speech are preserved across translation. We examine the hate speech detection ability of zero-shot and fine-tuned GPT. Our findings indicate that: (1) English has the highest performance and the code-switching scenario of English-Tamil being the lowest, (2) fine-tuning improves performance consistently across languages whilst translation yields mixed results. Through simple experimentation with original text and machine-translated text for hate speech detection along with a qualitative error analysis, this paper sheds light on the socio-cultural nuances and complexities of languages that may not be captured by automatic translation.

本研究解决了多语言环境中检测LGBTQIA+仇恨言论的挑战，尤其是机器翻译对仇恨言论细微差别的影响。通过对零样本与微调的GPT模型进行评估，发现英文表现最佳，而英泰代码混合情况最差，微调在各语言中表现出一致性提高，表明社会文化差异在自动翻译中可能未被捕捉。

“仇恨在翻译中失落了吗？”：多语言LGBTQIA+仇恨言论检测的评估