Contradictory Content

Contradictory content, encompassing inconsistencies and conflicting information within data or generated text, is a significant challenge across various AI applications. Current research focuses on detecting and mitigating contradictions in large language models (LLMs) using techniques like red teaming, prompt engineering, and enhanced factuality metrics, often incorporating aspects of natural language inference and reasoning. This work aims to improve the reliability and safety of AI systems, particularly in high-stakes domains like medicine and science, by enhancing their ability to handle ambiguous or conflicting information. The ultimate goal is to develop more robust and trustworthy AI systems capable of producing consistent and accurate outputs.