AI LLM Integration

Abstract

Abstract: This study investigates the integration and utilization of advanced language models in diverse applications, ranging from creative content generation to sentiment analysis, emotion analysis, text completion, and article summarization. The study leverages state-of-the-art models from Hugging Face and OpenAI, exploring their capacities in addressing distinct facets of natural language processing. The primary focus is on the capabilities and limitations of language models in generating creative content, discerning sentiment in text, identifying emotional nuances, completing text prompts, and summarizing articles. Additionally, the research addresses the challenge of link processing within language models by implementing a dual-API approach for effective data retrieval and summarization. Through a comprehensive analysis, this paper contributes insights into the broader applicability of large language models, shedding light on their potential impact on creative and analytical tasks. It explores the nuances of emotion and sentiment detection, assesses the effectiveness of text completion, and evaluates the practicality of summarizing articles using language models. The findings presented in this research enhance our understanding of the capabilities and challenges associated with advanced language models. Moreover, the study provides a foundation for future research and optimizations in the integration of language models, offering potential avenues for improvements in diverse applications.