Factual Error

Factual error in text generated by large language models (LLMs) is a significant research area aiming to improve the accuracy and reliability of AI-generated content. Current research focuses on developing methods for detecting and correcting these errors, employing techniques like ensemble prompting, external knowledge retrieval, and iterative constrained editing within various model architectures including transformer-based LLMs. These advancements are crucial for enhancing the trustworthiness of AI-generated summaries, reports, and other textual outputs across diverse applications, ranging from scientific literature review to healthcare and finance. The ultimate goal is to create more reliable and responsible AI systems.