LLM hallucination study - Potdar, Prateek Anish (Nanyang Technological University, 2025)
Large Language Models (LLMs) exhibit impressive generative capabilities but often produce hallucinations—outputs that are factually incorrect, misleading, or entirely fabricated. These hallucinations pose significant challenges in high-stakes applications such as medical diagnosis, legal reasoning, financial analysis, and scientific research, where factual accuracy is critical. As LLMs become increasingly integrated into real-world systems, mitigating hallucinations is essential to ensuring reliable, trustworthy, and ethically sound AI deployments. Without effective strategies to reduce hallucinations, AI-generated content risks contributing to misinformation, undermining user trust, and limiting the adoption of LLMs in professional domains.