Unverified50% confidenceFactExact time
Knowledge stored in large model parameters has a cutoff date and is prone to hallucination—generating content that appears plausible but is actually incorrect.
1
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50%
Confidence
Long-term
Relevance
9/1/2026
First Seen
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Related Claims
Verified幻觉（Hallucination）指大语言模型生成看似合理但实际错误或无依据的内容，是当前所有LLM的共性问题，根源在于模型本质上是基于概率的文本生成器而非知识检索系统78% similarVerifiedThe root cause of large model hallucination lies in the fact that the model's training objective is to predict the next most likely token, rather than to verify factual accuracy.76% similarUnverified斯坦福大学等机构的研究发现，当提示中包含误导性信息时，模型往往会在思维链中合理化错误答案，而非真实记录其受到干扰的推理过程75% similarPartially VerifiedModel Hallucination refers to when large language models generate content that appears plausible but is actually incorrect, delivered with high confidence despite lacking factual support.74% similarUnverifiedModel hallucination refers to models generating outputs that appear plausible but are actually incorrect, requiring robust rollback, retry, and human intervention mechanisms to address.73% similar
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