Planning a group outing can be a logistical nightmare, trying to balance everyone's preferences. But what if AI could step in and create the perfect itinerary? New research explores how Large Language Models (LLMs), the brains behind tools like ChatGPT, can be used to recommend points of interest (POIs) that a whole group will enjoy. This isn't just about suggesting popular spots; it's about understanding the complex dynamics of group decision-making. Researchers have developed a framework called LLMGPR that goes beyond simply aggregating individual preferences. It considers the sequence of past group visits, the time elapsed between visits, and even the geographical distance between POIs. Imagine a group of friends on a city break. LLMGPR can analyze their previous activities, like visiting a museum followed by lunch at a cafe, and then recommend a nearby park for a relaxing afternoon. This framework leverages the semantic understanding of LLMs. For example, if the group has visited several historical sites, the LLM can infer an interest in history and suggest other relevant POIs. To overcome the scarcity of group-level data (groups go out less often than individuals), LLMGPR cleverly uses individual check-in data to enhance group recommendations. It also uses a self-supervised learning task, essentially training itself to predict the purpose of a visit (e.g., business trip, family vacation). This allows it to extract even more meaningful information from limited data. The results are promising. In tests, LLMGPR outperformed existing group recommendation methods, demonstrating its ability to generate more relevant and satisfying group itineraries. This research opens exciting possibilities for using AI to personalize group experiences. While challenges remain, such as further improving accuracy and addressing privacy concerns, this work takes a significant step towards AI-powered group trip planning, making outings more enjoyable and less stressful for everyone involved.