The Calibration Problem: Why Your LLMs Hallucinate

Description

Hallucinations aren’t glitches; they’re grading. OpenAI’s new paper argues we trained models to guess because our benchmarks punish uncertainty and reward smooth answers. If we want trustworthy systems, we must change the incentives. What builders can do now: • Score for uncertainty: track coverage-vs-accuracy; reward “I don’t know” and penalize confident wrong. • Gate on calibration: add a refusal head / logit-margin gate; monitor Brier/ECE and escalate to search or human review when unsure. • Require evidence: claim-evidence checks in RAG; if no support → abstain. • Show confidence to users: surface uncertainty bands and citations, not just text. • Buy smart: ask vendors for accuracy at X% coverage curves, not single scores. Paper: https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4aaa5/why-language-models-hallucinate.pdf #LLMEvaluation #AISafety #RAG #ProductManagement #LLMReliability #HumanInTheLoop #RediMinds #CreateTheFuture