From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data
The episode discusses a paper detailing how Large Language Model hallucinations stem from specific structural weaknesses in their design. The hosts explain that self-attention, Maximum Likelihood Estimation (MLE), and autoregressive decoding are the pillars of failure. They conclude that LLM errors are architectural flaws, not merely data issues, providing a diagnostic map for targeted engineering solutions.