Скачать или смотреть 🔍 Inside the Science of AI Hallucinations – What OpenAI Just Revealed (video)

💡 Introduction
Large language models still “hallucinate” — producing confident but false answers.
This OpenAI-backed study, Why Language Models Hallucinate, dives deep into why modern AI systems are statistically pushed to guess instead of admitting uncertainty.

🔑 Key Highlights
Statistical Origin of Hallucination – Pretraining naturally favors plausible completions even when uncertain, a side effect of density-estimation math.

Binary-Grading Problem – Post-training tests reward “guessing” behavior, reinforcing deceptive confidence.

The Is-It-Valid Model – Hallucinations emerge as predictable classification errors, not mysterious failures.

The Socio-Technical Solution – Revise benchmarks to reward honesty and explicitly allow uncertainty (“I don’t know”).

Beyond Punishment to Calibration – Proposes explicit confidence thresholds and evaluation reform to teach AI humility.

🌍 Why Watch
Discover the hidden forces inside AI training that cause hallucinations — and the practical path to reduce them.
If you’ve ever wondered why chatbots invent details, this video explains the science, not the speculation.

📢 Join the Conversation
Would you rather your AI admit uncertainty or take a confident guess?
Share your thoughts — what should “honesty” mean for intelligent systems?

🏛️Research Paper https://arxiv.org/pdf/2308.08155v2.pdf

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