Unverified50% confidenceFactExact time
When generating text, an LLM does not maintain a queryable factual database, but predicts the next word one by one based on context; information that is high-frequency, coherent and repeatedly appearing in the training data is reproduced more stably, while low-frequency, contradictory or fragmentary facts are easily smoothed by the language model's fluency into seemingly complete narratives.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedLLMs are fundamentally probability distribution systems trained on massive amounts of text, where each output is a sampling of the next most likely token rather than retrieval and reasoning based on real-world knowledge.79% similarVerifiedThe root cause of citation hallucination is that LLMs learn linguistic patterns by predicting the next word, and no factual database for real-time verification is embedded in the training process.78% similarUnverifiedLLMs perform impressively on tasks with abundant training data and clear patterns (such as common programming languages and general writing), but frequently make hard-to-detect errors in scenarios requiring precise reasoning, long chains of logic, or obscure specialized knowledge.75% similarUnverifiedLLM-based information extraction can be quickly adapted to different business scenarios through prompt engineering without retraining the model.74% similarVerifiedThe core capability of modern LLMs lies in 'next token prediction,' generating coherent text through statistical language patterns rather than truly understanding semantics.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1048344API
curl https://kongchang.com/api/v1/knowledge/claims/1048344MCP
get_claim(id=1048344)