CONTENT MODERATION FRAMEWORK FOR THE LLM-BASED RECOMMENDATION SYSTEMS

Abstract

Recommendation systems are integral to modern applications, personalization is bound to enhanced by Large Language Models (LLMs). However, their integration to these systems poses challenges, particularly in content moderation, essential for maintaining a safe user environment. Content moderation ensures relevance, trustworthiness, and safety by filtering inappropriate content. In past, some examples of AI backed tools raised concern about potential biases and legal implications. This paper explores the intersection of LLMs and recommendation systems, emphasizing the critical role of content moderation in mitigating risks. This work delves into historical content moderation papers, highlighting challenges in balancing contextual understanding, diversity, and compliance. This paper emphasizes the need for a tailored approach to address the dynamic nature of LLM-generated content for specific organization needs. This is an attempt to introduce a novel framework for LLM-generated content moderation, adapting to diverse business scopes, considering unique boundaries and dynamic nature of recommendations. This paper contributes a comprehensive exploration of intersection between LLMs, recommendation systems, and content moderation. The proposed framework aims to fill existing gaps, offering a dynamic solution for the intricate challenges posed by LLM-generated content in the rapidly evolving landscape of AI applications.