  Large language models (LLMs) have brought breakthroughs in tasks including
translation, summarization, information retrieval, and language generation,
gaining growing interest in the CHI community. Meanwhile, the literature shows
researchers' controversial perceptions about the efficacy, ethics, and
intellectual abilities of LLMs. However, we do not know how people perceive
LLMs that are pervasive in everyday tools, specifically regarding their
experience with LLMs around bias, stereotypes, social norms, or safety. In this
study, we conducted a systematic review to understand what empirical insights
papers have gathered about people's perceptions toward LLMs. From a total of
231 retrieved papers, we full-text reviewed 15 papers that recruited human
evaluators to assess their experiences with LLMs. We report different biases
and related concepts investigated by these studies, four broader LLM
application areas, the evaluators' perceptions toward LLMs' performances
including advantages, biases, and conflicting perceptions, factors influencing
these perceptions, and concerns about LLM applications.
