nadirah 👩🏾‍💻

🧠 🧩 🚦 📉 ⚙️ University of Sheffield argues LLM hallucinations are mathematically inevitable. And using confidence thresholds the way OpenAI proposes would cut hallucinations but break consumer UX and spike costs. The core claim is that next-token generation stacks errors across a sentence, so even with perfect data the total mistake rate grows. A language model builds a sentence word by word. At each step, it picks the next word it thinks is most likely. If it makes one small mistake early on, that mistake affects the words that come after it. The sentence then drifts further from the correct answer. Now compare that to a yes/no question. The model only has to pick between two options: “yes” or “no.” There is just one decision, so fewer chances for error. An "yes/no question" is like a baseline: one single prediction, no chain of dependencies. But a sentence is a long chain of predictions, and each link in the chain can go wrong. This is why the study says the error rate for full sentences will always be at least 2 times higher than for simple yes/no answers. Because in sentences, errors can accumulate word by word, instead of being contained in a single decision. In plain terms, incentives today still favor fast, cheap, confident replies over slower, cautious, correct ones, so hallucinations will stick around.

Good reads to consider if you’re serious about AI, power, and equity: AI Superpowers by Kai-Fu Lee The Deficit Myth by Stephanie Kelton Weapons of Math Destruction by Cathy O’Neil The Alignment Problem: Machine Learning and Human Values by Brian Christian