Understanding emotions from text, speech, and video is a complex challenge for AI. Imagine trying to decipher sarcasm or genuine joy from a sentence, a laugh, or a facial expression—it requires a deep understanding of context and even human ethics. Traditional AI models often struggle with this nuance. Now, researchers are exploring how Large Language Models (LLMs), known for their powerful text processing capabilities, can unlock a new level of emotional understanding. A new framework called "Lantern" cleverly combines the strengths of smaller, specialized AI models that analyze multimedia data (like speech and video) with the knowledge and reasoning power of LLMs. These smaller models provide initial emotion predictions and dimensional scores (like valence, arousal, and dominance) that act as helpful hints for the LLMs. The LLMs then use their vast knowledge to refine these initial predictions, taking into account the context of the conversation. To tackle the limitations of LLM context windows, Lantern uses a sliding window technique to break down long dialogues into smaller, manageable chunks. This gives the LLM multiple perspectives on the conversation, enabling it to grasp both local and global emotional cues. A clever weighting mechanism then combines these different perspectives to generate a final, refined emotion prediction. Experiments using Lantern with models like CORECT, SDT, GPT-4, and Llama-3.1-405B on the IEMOCAP dataset have shown promising results, significantly boosting emotion recognition accuracy. While the need for labeled data for both emotion classification and dimensional scores presents a challenge, Lantern offers a glimpse into a future where AI can truly understand the complexities of human emotion, opening doors to more empathetic and human-centered applications.