Caballero Hinojosa, Alberto2024-05-202024-05-202023https://hdl.handle.net/20.500.14468/14712In today’s interconnected global landscape, political news wields significant influence in shaping voter perceptions. Political news stands as a primary source of information for citizens in democratic societies. Understanding the intentions underlying news articles is indispensable for ensuring transparency in political discourse. It empowers the public to make informed decisions and to hold media platforms accountable for the information they disseminate, whether directly or indirectly. Precise identification of news intentions holds the potential to safeguard democratic processes, forming the core focus of this Master’s thesis. Accurate identification of news intentions assumes a central role in Natural Language Processing (NLP) for effective information analysis and categorization. This research project delves into the innovative concept of “Adaptive Learning Prompting”. It harnesses the formidable capabilities of Large Language Models (LLMs) for efficient news intention classification through iterative prompt engineering, with a specific emphasis on resource-constrained scenarios. Employing a dynamic evaluation methodology conducted in iterative batches, this research meticulously tracks model performance. Through trend analysis, the evolution of the proposed model architecture emerges as statistically significant, effectively demonstrating the adaptability inherent in this proposed approach. In summary, this Master’s thesis serves as a testament to the potential of LLMs in adaptive news intention identification. Particularly noteworthy is the “Adaptive Learning Prompting” strategy. While specific performance metrics may exhibit variations, the adaptive approach underscores the feasibility of iterative feedback prompting for operational enhancements, especially in resource-scarce scenarios. These findings transcend the realm of news intention analysis, offering broader applications in NLP, while shedding light on the reasoning capabilities intrinsic to LLMs, thereby amplifying their utility.eninfo:eu-repo/semantics/openAccesstesis de maestría