Unverified50% confidenceSolutionExact time
大语言模型基于下一个Token预测机制，天然导致幻觉问题，RAG和Function Calling是缓解幻觉的工程方案
1
Sources
50%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
AI大模型入门：核心概念、发展历程与国产模型格局
Related Claims
VerifiedStrategies for mitigating LLM hallucination include retrieval-augmented generation (RAG), chain-of-thought prompting, and requiring the model to output confidence scores.78% similarUnverifiedCitation hallucination is a special subtype of the hallucination problem in large language models, with its technical root cause being that LLMs learn the probability distribution of token sequences rather than precise memorization of real-world facts.74% similarVerifiedThe hallucination problem in large language models stems from their nature as statistical probability-based sequential prediction systems, where generated tokens are based on conditional probability maximization rather than genuine logical reasoning.74% similarVerifiedThe technical root cause of hallucination in large models lies in the autoregressive generation mechanism, where the model predicts only the statistically most likely next token based on the preceding context; researchers divide hallucinations into intrinsic hallucinations and extrinsic hallucinations.73% similar
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