This research investigates the types of knowledge that can be elicited through the integration of Large Language Models (LLMs) into Games With A Purpose (GWAPs). By using a literature survey using the PRISMA framework, we synthesize findings from different studies to find patterns and gaps in the existing research. The survey focuses on the utilization of LLMs within GWAPs and examines how these models diversify the types of knowledge elicited. The findings indicate that LLMs significantly enhance the knowledge elicitation capabilities of GWAPs, transforming them into more interactive and effective tools. We found that different types of knowledge can be elicited, such as contextual insights, factual information, semantic associations and experimental knowledge.