A Comprehensive Review of Supervised Fine-Tuning for Large Language Models in Creative Applications and Content Moderation

This article examines the pivotal role of Supervised Fine-Tuning (SFT) in adapting Large Language Models (LLMs) for specialized tasks, with a focus on story generation, content moderation, and domain-specific applications. SFT refines LLMs by leveraging labeled data to enhance task-specific performance, yet challenges such as data scarcity, maintaining diversity, and ensuring coherence persist. In story generation, balancing creativity with narrative consistency remains complex, though symbolic techniques and retrieval-augmented frameworks are advancing solutions. Content moderation benefits from LLMs' ability to detect harmful content, but challenges related to scalability, detection, and interpretability remains unresolved. Emerging trends, including data-efficient fine-tuning, self-supervised learning, and collaborative multi-LLM approaches, are addressing these limitations. This review highlights the need for ethical and culturally aware moderation frameworks while emphasizing future research directions focused on optimizing fine-tuning processes, improving generative quality, and enhancing model transparency. These advancements will drive both innovative and responsible AI applications across diverse domains.