Verified75% confidenceFactExact time
The 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.
5
Sources
75%
Confidence
Long-term
Relevance
7/8/2026
First Seen
Sources
元认知反馈:用强化学习让大模型准确表达不确定性
hackernewshackernews7/7/2026
Related Claims
UnverifiedModel hallucination refers to models generating outputs that appear plausible but are actually incorrect, requiring robust rollback, retry, and human intervention mechanisms to address.81% similarUnverifiedThe essence of model hallucination is probabilistic completion by the model outside the distribution of training data; core methods for reducing hallucination include providing contextual anchors, requiring the model to explicitly cite sources, and combining RAG to provide factual grounding.81% 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.81% similarUnverifiedHallucinations stem from the model's optimization objective itself—the model is trained to generate linguistically coherent and statistically plausible continuations, not factually accurate answers.81% similarVerified大模型幻觉的技术本质在于模型在训练时学习的是语言模式的概率分布而非事实本身78% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/279895API
curl https://kongchang.com/api/v1/knowledge/claims/279895MCP
get_claim(id=279895)