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The rising popularity of ChatGPT and other AI-powered large language models(LLMs) has led to increasing studies highlighting their susceptibility tomistakes and biases. However, most of these studies focus on models trained onEnglish texts. Taking an innovative approach, this study investigates politicalbiases in GPT's multilingual models. We posed the same question abouthigh-profile political issues in the United States and China to GPT in bothEnglish and simplified Chinese, and our analysis of the bilingual responsesrevealed that GPT's bilingual models' political "knowledge" (content) and thepolitical "attitude" (sentiment) are significantly more inconsistent onpolitical issues in China. The simplified Chinese GPT models not only tended toprovide pro-China information but also presented the least negative sentimenttowards China's problems, whereas the English GPT was significantly morenegative towards China. This disparity may stem from Chinese state censorshipand US-China geopolitical tensions, which influence the training corpora of GPTbilingual models. Moreover, both Chinese and English models tended to be lesscritical towards the issues of "their own" represented by the language used,than the issues of "the other." This suggests that GPT multilingual modelscould potentially develop a "political identity" and an associated sentimentbias based on their training language. We discussed the implications of ourfindings for information transmission and communication in an increasinglydivided world.

Red AI? Inconsistent Responses from GPT3.5 Models on Political Issues in the US and China