Unverified50% confidenceFactExact time
当输入存在歧义时LLM不会报错，而是沿训练数据中最高频的语义路径继续生成，用统计上最常见的答案填补需求空白
1
Sources
50%
Confidence
Long-term
Relevance
7/17/2026
First Seen
Sources
Related Claims
UnverifiedLLM在面对开放性问题时倾向于输出训练数据中频率最高的安全表达，即听起来正确但信息密度极低的通用结论75% similarUnverified当用户需求描述模糊时，LLM会倾向于从训练数据中召回看起来专业的复杂架构方案，导致过度设计70% similarUnverifiedThe true difficulty of model fine-tuning lies in the construction and cleaning of high-quality training data, preventing catastrophic forgetting, and inference deployment after quantization.69% similarUnverifiedLarge models output the most likely next word rather than precise retrieval of facts, which leads to hallucinations in domains with sparse coverage in the training data.69% similar
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