Hallucinations in Artificial Intelligence: Origins, Detection, and Mitigation

Brahmaleen Kaur Sidhu

Abstract: Artificial intelligence hallucinations, a phenomenon where artificial intelligence models generate content that is plausible but factually incorrect, have become a critical challenge in artificial intelligence research and deployment. This paper explores the concept of hallucinations in artificial intelligence, questioning the validity of the term itself and its implications within the artificial intelligence domain. It delves into the various types and causes of artificial intelligence hallucinations, identifying both intrinsic and extrinsic factors that contribute to this issue across diverse artificial intelligence applications. Furthermore, it discusses methods for detecting hallucinations, highlighting advancements in diagnostic tools and evaluation metrics. Finally, it reviews mitigation strategies, ranging from architectural modifications to post-hoc correction mechanisms, aimed at reducing the frequency and impact of hallucinations. Through this comprehensive analysis, the paper seeks to provide a clearer understanding of artificial intelligence hallucinations and establish a foundation for future research and solutions in this area.

Keywords: Artificial Intelligence Hallucinations, Artificial Intelligence Reliability, Hallucination Detection, Hallucination Mitigation Strategies