Understanding the unspoken feelings hidden within text is a complex challenge for AI. Implicit sentiment analysis (ISA) grapples with deciphering opinions and emotions that aren't explicitly stated. Now, researchers are exploring how large language models (LLMs) can unlock this subtle world of meaning. A new approach called MT-ISA combines the power of LLMs with a clever multi-tasking strategy. It uses LLMs to generate related sentiment elements, like aspects and opinions, filling in the missing pieces of the sentiment puzzle. But simply generating more data isn't enough. MT-ISA introduces automatic weight learning (AWL) to prioritize reliable information and crucial tasks, dynamically balancing the contributions of different parts of the model. This innovative approach helps LLMs of all sizes better understand the nuances of implicit sentiment, paving the way for more insightful and emotionally intelligent AI systems. The research shows that this dynamic multi-tasking approach achieves state-of-the-art results on benchmark datasets. While promising, challenges remain in refining confidence scores and ensuring the stability of the automatic weight learning process. Future work will likely focus on improving these areas and exploring the potential of this approach in a broader range of NLP applications.