Скачать или смотреть OpenAI Explains: Why Language Models Hallucinate & How to Reduce Them

Discover OpenAI's insights into why large language models, including ChatGPT and GPT-5, generate plausible but false statements – a phenomenon known as hallucination. This video explores OpenAI's new research, which argues that standard training and evaluation procedures often reward guessing over acknowledging uncertainty, thereby perpetuating hallucinations. Learn how evaluation methods that solely focus on accuracy can encourage models to guess rather than indicate uncertainty, even though errors are considered worse than abstentions. We also delve into how hallucinations originate from the next-word prediction process during pretraining, particularly with arbitrary, low-frequency facts. OpenAI proposes a better approach to grading evaluations, suggesting that confident errors should be penalised more than uncertainty, and appropriate expressions of uncertainty should receive partial credit. The sources highlight that hallucinations are not inevitable, as language models can abstain when uncertain, and understanding the statistical mechanisms behind them is key to reduction. Even as models like GPT-5 show significantly fewer hallucinations, OpenAI continues to work hard on further decreasing these confident errors to make AI systems more useful and reliable.

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