Verified70% confidenceFactExact time
Large 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.
4
Sources
70%
Confidence
Long-term
Relevance
7/24/2026
First Seen
Sources
零基础入门大模型微调：原理、场景与实战路径
Related Claims
VerifiedThe probability of hallucination increases significantly when contradictory information exists in the training data or when the model is asked about domains that are sparsely represented in the training data.83% similarVerifiedWith only a relatively small amount of targeted training data, it is possible to make a large language model give entirely incorrect answers to clearly stated facts78% similarVerifiedThe 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.77% similarUnverified模型规模越大，幻觉的表述往往越流畅、越具迷惑性，识别难度反而上升77% similarVerifiedThe three main causes of hallucinations in large models are training data noise, the probability-first mechanism, and insufficient data timeliness.76% similar
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