LLM In-Context Learning System

An LLM In-Context Learning System is an in-context learning system that enables large language models to adapt through example demonstrations within prompt context without parameter updates.

• AKA: Few-Shot Learning System, LLM Example-Based Learning System, In-Context Adaptation System, Prompt-Based Learning System.
• Context:
  • It can typically manage LLM Example Selection through llm similarity metrics, llm relevance scoring, and llm diversity criterions.
  • It can typically optimize LLM Context Window Usage via llm example ordering, llm context compression, and llm information density.
  • It can typically implement LLM Few-Shot Templates using llm structured formats, llm instruction prefixes, and llm example separators.
  • It can typically support LLM Zero-Shot Learning through llm task instructions, llm role specifications, and llm output constraints.
  • It can typically enable LLM One-Shot Learning with llm single examples, llm pattern demonstrations, and llm format guidance.
  • It can often facilitate LLM Chain-of-Thought Prompting via reasoning steps, intermediate calculations, and explanation generation.
  • It can often coordinate LLM Dynamic Example Retrieval using vector databases, semantic search, and contextual matching.
  • It can often provide LLM Example Quality Assessment through relevance checks, accuracy validation, and bias detection.
  • It can range from being a Static LLM In-Context Learning System to being a Dynamic LLM In-Context Learning System, depending on its example selection strategy.
  • It can range from being a Manual LLM In-Context Learning System to being an Automated LLM In-Context Learning System, depending on its example curation method.
  • It can range from being a Domain-Specific LLM In-Context Learning System to being a General-Purpose LLM In-Context Learning System, depending on its application scope.
  • It can range from being a Simple LLM In-Context Learning System to being a Sophisticated LLM In-Context Learning System, depending on its retrieval complexity.
  • ...
• Example(s):
  • Commercial LLM In-Context Learning Systems, such as:
    • OpenAI Few-Shot API, which provides llm example formatting with llm completion generation.
    • Claude Context Learning, which uses llm example demonstrations for llm behavior adaptation.
    • Google PaLM API, which supports llm multi-shot learning with llm task adaptation.
  • Research LLM In-Context Learning Systems, such as:
    • KATE System, which implements llm k-nearest example retrieval for llm few-shot learning.
    • EPR System, which uses llm efficient prompt retrieval with llm dense retrievers.
  • ...
• Counter-Example(s):
  • Fine-Tuning System, which modifies model parameters rather than using llm context examples.
  • Transfer Learning System, which requires gradient updates instead of llm prompt-based adaptation.
  • Supervised Training System, which needs backpropagation rather than llm in-context demonstration.
• See: Large Language Model, Few-Shot Learning, LLM Prompt Engineering System, Automated Few-Shot Example Discovery Process, LLM Example Selection Strategy, RAG Framework, LLM Evaluation Platform, Context Window Management, LLM Prompt Optimization Pipeline.

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