The Digital Press

The article commentary discusses the inherent challenges in large language models (LLMs) related to their reasoning processes and tendency to produce factually incorrect but coherent outputs, often described as "hallucinations" or "bullshit."

Why it matters: LLMs' reasoning can increase error rates by generating more tokens, each potentially introducing mistakes, compounding inaccuracies.

The big picture: LLMs prioritize coherent narratives over truth, reflecting their design to transform text inputs into plausible outputs without true understanding.

The other side: Some users find LLMs' confident but incorrect responses frustrating, especially when suggested optimizations degrade performance.

Commenters say: Many agree LLM "hallucinations" are better seen as purposeful fabrication rather than perception errors, highlighting the need for careful interpretation.