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% similarVerified大语言模型的幻觉问题源于其本质是基于统计概率的序列预测系统，生成的Token基于条件概率最大化而非真正的逻辑推理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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