Predicting when someone will finish speaking, a seemingly simple task for humans, presents a significant challenge for AI. This ability, known as turn-taking prediction, is crucial for building natural-sounding conversational agents. Researchers are exploring innovative ways to tackle this challenge, moving beyond simply analyzing text and incorporating audio cues. A new study explores a multi-modal approach called Lla-VAP, which combines the language understanding of large language models (LLMs) like Llama with the temporal precision of voice activity projection (VAP) models. VAP analyzes audio to anticipate when someone might stop talking based on pauses and changes in tone. The researchers tested Lla-VAP on two datasets: one with scripted conversations about movies and another with unscripted, informal dialogues. They found that predicting the end of a turn is much easier than predicting pauses *within* a turn. Think about it—even humans can struggle to anticipate those subtle pauses mid-sentence. While the model showed promising results for predicting complete turns, especially when combining audio and text information, within-turn predictions remain a significant hurdle. Interestingly, the way LLMs are prompted plays a crucial role. Framing the task in a conversational way, like asking the LLM if someone has finished their turn, significantly improved performance compared to using more technical language. This research highlights the complexities of building truly conversational AI. Accurately predicting turn-taking is essential for avoiding awkward interruptions and creating smoother, more natural interactions. While challenges remain, multi-modal approaches like Lla-VAP offer a promising path toward building AI that can understand not only *what* we say, but also *when* we say it.