Large Language Models (LLMs) are increasingly applied in recommender systems aimed at both individuals and groups. Previously, Group Recommender Systems (GRS) often used social choice-based aggregation strategies to derive a single recommendation based on the preferences of multiple people. In this paper, we investigate under which conditions language models can perform these strategies correctly based on zero-shot learning and analyse whether the formatting of the group scenario in the prompt affects accuracy. We specifically focused on the impact of group complexity (number of users and items), different LLMs, different prompting conditions, including In-Context learning or generating explanations, and the formatting of group preferences. Our results show that performance starts to deteriorate when considering more than 100 ratings. However, not all language models were equally sensitive to growing group complexity. Additionally, we showed that In-Context Learning (ICL) can significantly increase the performance at higher degrees of group complexity, while adding other prompt modifications, specifying domain cues or prompting for explanations, did not impact accuracy. We conclude that future research should include group complexity as a factor in GRS evaluation due to its effect on LLM performance. Furthermore, we showed that formatting the group scenarios differently, such as rating lists per user or per item, affected accuracy. All in all, our study implies that smaller LLMs are capable of generating group recommendations under the right conditions, making the case for using smaller models that require less computing power and costs.

本研究揭示了在多用户偏好的群体推荐系统中，语言模型在何种条件下能够正确实现社会选择基于的聚合策略。我们发现，当评价超过100个时，模型性能显著下降，但小型语言模型在适当条件下仍能有效生成群体推荐，提示未来研究需考虑群体复杂性作为GRS评估的一个因素。

增长的群体复杂性的陷阱：大型语言模型与基于社会选择的群体推荐聚合