In this episode, we discuss Why Language Models Hallucinate by The authors of the paper are: - Adam Tauman Kalai - Ofir Nachum - Santosh S. Vempala - Edwin Zhang. The paper explains that hallucinations in large language models arise because training and evaluation reward guessing over admitting uncertainty, framing the issue as errors in binary classification. It shows that models become incentivized to produce plausible but incorrect answers to perform well on benchmarks. The authors propose that addressing hallucinations requires changing how benchmarks are scored, promoting more trustworthy AI by discouraging penalization of uncertain responses.