Survey on AI Powered Transformative Post Generator for Social Media using LLM and Explicit Filter

GURAKALA TEJARSHINI; RAYI MANASA; GUMMIDI NAGARAJU; R.AFSAR AHAMMAD; VELURU SYAM KUMAR

Survey on AI Powered Transformative Post Generator for Social Media using LLM and Explicit Filter

Authors: GURAKALA TEJARSHINI, RAYI MANASA, GUMMIDI NAGARAJU, R.AFSAR AHAMMAD, VELURU SYAM KUMAR

Unique Paper ID: 177961
Volume: 11
Issue: 12
PageNo: 1725-1732

Keywords: Social Media Post Generator Unified prompt learning parameter efficient fine-tuning prompt tuning Prompt Learning Reinforcement Learning Few-shot Image Classification Vision-Language Pre trained Models.

Abstract:
This research paper presents an advanced Social Media post generator that utilizes cutting-edge Language Models (LLMs) to discern user intent and generate contextually relevant content. The system meticulously analyzes user input to craft professional and engaging Social Media posts. This tool has significant potential for individuals and businesses seeking to enhance content creation and maintain a consistent social media presence. Our paper encompasses natural language processing techniques, prompt engineering, and a user-friendly interface design. It includes an explicit content filter to ensure the production of appropriate and professional content in line with Social Media standards. This paper provides a comprehensive overview of our development process, technology stack, user intent recognition methodology, and user interface design. We also discuss the practical applications, benefits, and implications of this tool for improving online presence and content creation efficiency within professional social media platforms. This research contributes to the growing field of AI-driven content generation in the context of professional networking platforms.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 1725-1732

Survey on AI Powered Transformative Post Generator for Social Media using LLM and Explicit Filter

Available:https://ijirt.org/Article?manuscript=177961

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