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Large language models (LLMs) have brought breakthroughs in tasks includingtranslation, summarization, information retrieval, and language generation,gaining growing interest in the CHI community. Meanwhile, the literature showsresearchers' controversial perceptions about the efficacy, ethics, andintellectual abilities of LLMs. However, we do not know how lay people perceiveLLMs that are pervasive in everyday tools, specifically regarding theirexperience with LLMs around bias, stereotypes, social norms, or safety. In thisstudy, we conducted a systematic review to understand what empirical insightspapers have gathered about people's perceptions toward LLMs. From a total of231 retrieved papers, we full-text reviewed 15 papers that recruited humanevaluators to assess their experiences with LLMs. We report different biasesand related concepts investigated by these studies, four broader LLMapplication areas, the evaluators' perceptions toward LLMs' performancesincluding advantages, biases, and conflicting perceptions, factors influencingthese perceptions, and concerns about LLM applications.

People's Perceptions Toward Bias and Related Concepts in Large Language Models: A Systematic Review