Hallucination Rate

Hallucination, the generation of factually incorrect or nonsensical outputs by large language models (LLMs) and other generative AI systems, is a critical research area aiming to improve model reliability and trustworthiness. Current research focuses on developing robust evaluation benchmarks across diverse modalities (text, image, audio) and model architectures, including both open-set and real-world data-driven approaches to quantify hallucination rates and identify their underlying causes. These efforts are crucial for advancing the development of more reliable AI systems, impacting various applications from question answering and machine translation to multimodal reasoning and speech-to-text transcription. Improved understanding and mitigation of hallucinations are essential for ensuring the safe and responsible deployment of generative AI.