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.
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- 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.
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Example(s):
- 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.
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Counter-Example(s):
- 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.