Fine Tuning Large Language Models For Guardrailing

Abstract

The rapid dissemination of information through the internet has transformed how individuals access healthcare knowledge. However, this convenience comes with a significant drawback the proliferation of misinformation. Inaccurate health information can lead to detrimental outcomes, including misinformed decisions, increased anxiety, and harmful behaviors. Large Language Models (LLMs), such as those built on transformer architectures, have demonstrated exceptional proficiency in processing and generating human language. Despite their capabilities, these models are not immune to generating biased, misleading, or even harmful content, particularly when deployed in sensitive domains like healthcare. This research addresses the critical challenge of misinformation by finetuning LLMs to integrate robust guardrails. The goal is to create a framework that ensures generated healthcare content is not only accurate but also safe and aligned with ethical standards. The proposed approach involves leveraging state-of-the-art Natural Language Processing (NLP) techniques, coupled with domain-specific training, to enhance the reliability of LLM outputs. By focusing on healthcare, a domain where accuracy is paramount, the study emphasizes the necessity of tailored finetuning strategies to prevent the spread of incorrect or harmful information. Key methodologies employed in this research include supervised fine-tuning, active learning, and ensemble learning. These methods are designed to iteratively improve the model's ability to identify and filter misinformation. Supervised fine-tuning involves curating a high-quality dataset of healthcare information, including both correct and incorrect examples, to train the model on distinguishing reliable content. Active learning strategies engage human reviewers to refine the model's decision-making process, while ensemble learning aggregates predictions from multiple models to enhance overall robustness and reduce errors. The expected outcome is a finely tuned LLM that can assist users in navigating the vast landscape of online healthcare information, providing guidance that aligns with credible sources and professional standards. By deploying such a system, this research aims to mitigate the risks associated with misinformation, promoting public health and fostering trust in digital health information. In conclusion, the project underscores the importance of integrating strong guardrails into LLMs, especially in critical areas like healthcare. By refining these models to prioritize accuracy and ethical considerations, we take a significant step toward a safer, more reliable digital information ecosystem.