LLM Capability

An LLM Capability is an AI system capability for a large language model.

• Context:
  • It can (typically) enable Natural Language Processing through neural computation.
  • It can (typically) support Language Understanding through contextual processing.
  • It can (typically) perform Language Generation through token prediction.
  • It can (typically) handle Language Tasks through model inference.
  • It can (often) adapt to New Domains through transfer learning.
  • It can (often) improve with Model Scale through emergence.
  • It can (often) benefit from Instruction Tuning through prompt engineering.
  • ...
  • It can range from being a Proven LLM Capability to being a Hypothetical LLM Capability, depending on its verification status.
  • It can range from being a Core LLM Capability to being an Advanced LLM Capability, depending on its complexity level.
  • It can range from being a General Language Capability to being a Domain-Specific Capability, depending on its specialization scope.
  • ...
• Examples:
  • Basic LLM Capabilities, such as:
    • Text Completion for sentence continuation.
    • Question Answering for information retrieval.
    • Summarization for text compression.
    • Translation for language conversion.
  • Advanced LLM Capabilities, such as:
    • Reasoning for logical deduction.
    • Code Generation for programming tasks.
    • Creative Writing for content creation.
    • Dialog Management for conversation flow.
  • Emerging LLM Capabilities, such as:
    • Multi-Modal Processing for mixed input types.
    • Tool Use for external system interaction.
    • Self-Improvement for capability enhancement.
  • ...
• Counter-Examples:
  • Traditional NLP Capability, which lacks neural foundation.
  • Rule-Based Language Capability, which relies on predefined patterns rather than learned behavior.
  • Statistical Language Capability, which uses frequency analysis without deep learning.
• See: LLM Task, LLM Performance, LLM Architecture, LLM Training, LLM Evaluation.

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