Understanding Why Language Models Hallucinate

The article discusses the nature of hallucinations in language models, emphasizing that not all outputs are hallucinations and that the term needs careful definition. It highlights the distinction between models predicting next tokens and generating false information, and debates whether all outputs can be considered hallucinations. The conversation also covers challenges in reducing hallucinations, the importance of proper evaluation, and philosophical questions about AI understanding and truth. Overall, it stresses that hallucinations are inherent to probabilistic models like LLMs, and efforts should focus on minimizing them rather than expecting complete elimination.