Verified70% confidenceSolutionExact time
Model hallucination refers to models generating outputs that appear plausible but are actually incorrect, requiring robust rollback, retry, and human intervention mechanisms to address.
4
Sources
70%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
AI Agent Builder: A Complete Hands-On Guide from Zero to Enterprise-Grade Delivery
bilibiliAIAgent搭建7/11/2026
Related Claims
VerifiedHallucinations stem from the model's optimization objective itself—the model is trained to generate linguistically coherent and statistically plausible continuations, not factually accurate answers.83% 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.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.80% similarUnverifiedReAct relies entirely on the reasoning capability of large models; once the model hallucinates or falls into an incorrect path, it continuously consumes Tokens without being able to self-correct, and production environments typically need to incorporate human intervention or a maximum step limit.78% similarUnverifiedBy injecting fabricated reasoning, it is possible to forge a model's thought process and induce it to output content it otherwise should not, constituting a form of jailbreak attack.77% similar
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