대규모 언어 모델 (LLM)

거대 텍스트 기반 AI.

번역 대기 중인 콘텐츠입니다. 영어 버전을 표시하고 있습니다.

## The Hallucination Problem
LLMs do not have a 'database' of facts; they have a mathematical model of likelihood. When an LLM 'hallucinates,' it isn't lying—it is simply following a path of high statistical probability that happens to be factually wrong. Preventing hallucinations currently requires RAG (Retrieval-Augmented Generation), where the model is forced to look up external, trusted documents before answering.

        graph LR
  Center["대규모 언어 모델 (LLM)"]:::main
  Pre_transformer["transformer"]:::pre --> Center
  click Pre_transformer "/terms/transformer"
  Pre_deep_learning["deep-learning"]:::pre --> Center
  click Pre_deep_learning "/terms/deep-learning"
  Pre_natural_language_processing["natural-language-processing"]:::pre --> Center
  click Pre_natural_language_processing "/terms/natural-language-processing"
  Center --> Child_context_window["context-window"]:::child
  click Child_context_window "/terms/context-window"
  Center --> Child_hallucination_ai["hallucination-ai"]:::child
  click Child_hallucination_ai "/terms/hallucination-ai"
  Rel_prompt_engineering["prompt-engineering"]:::related -.-> Center
  click Rel_prompt_engineering "/terms/prompt-engineering"
  Rel_model_distillation["model-distillation"]:::related -.-> Center
  click Rel_model_distillation "/terms/model-distillation"
  Rel_multimodal_ai["multimodal-ai"]:::related -.-> Center
  click Rel_multimodal_ai "/terms/multimodal-ai"
  classDef main fill:#7c3aed,stroke:#8b5cf6,stroke-width:2px,color:white,font-weight:bold,rx:5,ry:5;
  classDef pre fill:#0f172a,stroke:#3b82f6,color:#94a3b8,rx:5,ry:5;
  classDef child fill:#0f172a,stroke:#10b981,color:#94a3b8,rx:5,ry:5;
  classDef related fill:#0f172a,stroke:#8b5cf6,stroke-dasharray: 5 5,color:#94a3b8,rx:5,ry:5;
  linkStyle default stroke:#4b5563,stroke-width:2px;

      

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## The Hallucination Problem
LLMs do not have a 'database' of facts; they have a mathematical model of likelihood. When an LLM 'hallucinates,' it isn't lying—it is simply following a path of high statistical probability that happens to be factually wrong. Preventing hallucinations currently requires RAG (Retrieval-Augmented Generation), where the model is forced to look up external, trusted documents before answering.

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