The video explains that hallucinations are ingrained in the models' construction, functioning more as features than bugs. This is compared to human behavior, where guessing on a test might be rewarded, leading models to guess rather than admit uncertainty. The core issue is the absence of a system that rewards models for expressing uncertainty or providing partially correct answers. The proposed solution involves creating models that only answer questions when they meet a certain confidence threshold and implementing a new evaluation system. This system would reward correct answers, penalize incorrect ones, and assign a neutral score for "I don't know" responses. The video concludes by suggesting that the solution lies in revising how models are evaluated and how reinforcement learning is applied. (summary provided in part by Gemini 2.5 Pro)
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