#4736 LLM Hallucinations & Mitigation
#4736 LLM Hallucinations & Mitigation #4736 What causes LLMs to hallucinate? This is for informational purposes only. For medical advice or diagnosis, consult a professional. LLM hallucinations—generating plausible-sounding but factually false, ungrounded, or internally inconsistent statements—stem from a combination of statistical training objectives, data compression, alignment incentives, and inference mechanics. 1. The Core Objective: Next-Token Prediction vs. Truth Verification LLMs are trained primarily as autoregressive probability models. Their objective function during pre-training is to minimize cross-entropy loss over a sequence of tokens: P ( w 1 , w 2 , … , w n ) = i = 1 ∏ n P ( w i ∣ w 1 , … , w i − 1 ) Statistical Plausibility over Factuality: The model learns grammatical structure, style, and high-probability associative transitions between words. It optimizes for what text is most likely to follow , not whether that text maps to an externally ve...