LLM Error

An LLM Error is an AI system error that occurs when large language models produce incorrect, inconsistent, or inappropriate outputs during text generation or reasoning tasks.

• AKA: Large Language Model Error, LLM Failure, Language Model Error, LLM Output Error.
• Context:
  • It can typically manifest in Text Generation through output deviations.
  • It can typically arise from Training Data Limitations or architectural constraints.
  • It can typically impact User Trust in AI systems.
  • It can typically require Error Detection Methods for quality assurance.
  • It can often be mitigated through Prompt Engineering or model fine-tuning.
  • It can often correlate with Task Complexity and domain specificity.
  • It can often propagate through Multi-Turn Conversations without error correction.
  • It can range from being a Syntactic LLM Error to being a Semantic LLM Error, depending on its linguistic level.
  • It can range from being a Detectable LLM Error to being a Subtle LLM Error, depending on its observability.
  • It can range from being a Recoverable LLM Error to being a Catastrophic LLM Error, depending on its impact severity.
  • It can range from being a Domain-Specific LLM Error to being a Universal LLM Error, depending on its occurrence scope.
  • ...
• Example:
  • Content Generation LLM Errors, such as:
    • LLM Hallucination Error for fabricated information.
    • LLM Conceptual Conflation Error for concept confusion.
    • LLM Repetition Error for circular outputs.
  • Reasoning LLM Errors, such as:
    • LLM Logic Error for invalid inferences.
    • LLM Consistency Error for self-contradictions.
  • Knowledge LLM Errors, such as:
    • LLM Factual Error for incorrect facts.
    • LLM Temporal Error for anachronistic claims.
  • ...
• Counter-Example:
  • Human Error, which stems from cognitive limitations rather than algorithmic flaws.
  • Database Error, which involves data storage rather than language generation.
• See: AI System Error, LLM Limitation, LLM Conceptual Conflation Error, LLM Hallucination Error, LLM Bias, Error Detection, Natural Language Processing Error, Machine Learning Error.

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