Neural Network based Approach for Content Generation: Comprehensive Analysis of LLM Models

This paper presents a detailed comparative analysis of Large Language Models (LLMs) for automated content creation on social media, focusing on GPT-3.5, LLaMA 3.1, Mixtral 8x7B, Mistral Nemo 12B, and Gemma 2 (9B). As social media continues to evolve, creating platform-specific, engaging, and scalable content has become critical for both personal and business application. The study examines the selection criteria for these models, including scalability, support for multiple languages, and ethical content moderation, while highlighting the importance of fine-tuning and prompt engineering. Furthermore, this paper evaluates the performance of LLMs on both short and long-form content, exploring their effect on user engagement, retention, and factual accuracy. Using evaluation metrics such as ROUGE, sentiment analysis, and readability scores, the paper aims to highlight the strengths and weaknesses of each model, providing a basis for optimizing content creation for social media platforms.