LLM Example Selection Strategy

An LLM Example Selection Strategy is a selection strategy that determines optimal examples for large language model in-context learning through relevance criteria and diversity constraints.

• AKA: Few-Shot Example Selection Strategy, In-Context Example Selection Method, Demonstration Selection Strategy, Prompt Example Selection Algorithm.
• Context:
  • It can typically employ LLM Similarity-Based Selection using llm embedding distances, llm semantic similarity, and llm feature matching.
  • It can typically implement LLM Diversity-Based Selection through llm coverage metrics, llm representative sampling, and llm cluster-based selection.
  • It can typically utilize LLM Task-Specific Selection via llm performance prediction, llm difficulty matching, and llm error analysis.
  • It can typically support LLM Dynamic Selection with llm contextual adaptation, llm user preferences, and llm real-time retrieval.
  • It can typically enable LLM Hybrid Selection combining llm multiple criterions, llm weighted scoring, and llm ensemble methods.
  • It can often optimize LLM Example Ordering through curriculum learning, progressive difficulty, and logical sequences.
  • It can often manage LLM Example Budget via token limitations, cost constraints, and efficiency trade-offs.
  • It can often evaluate LLM Selection Effectiveness using downstream performance, generalization metrics, and robustness tests.
  • It can range from being a Random LLM Example Selection Strategy to being a Sophisticated LLM Example Selection Strategy, depending on its selection complexity.
  • It can range from being a Static LLM Example Selection Strategy to being an Adaptive LLM Example Selection Strategy, depending on its adjustment capability.
  • It can range from being a Single-Criterion LLM Example Selection Strategy to being a Multi-Criterion LLM Example Selection Strategy, depending on its selection factors.
  • It can range from being a Offline LLM Example Selection Strategy to being an Online LLM Example Selection Strategy, depending on its computation timing.
  • ...
• Example(s):
  • Retrieval-Based LLM Example Selection Strategys, such as:
    • BM25 Selection, which uses term frequency for text matching.
    • Dense Retrieval Selection, which employs neural embeddings for semantic search.
    • Contrastive Selection, which identifies discriminative examples via contrast learning.
  • Learning-Based LLM Example Selection Strategys, such as:
    • Reinforcement Learning Selection, which learns selection policys through reward signals.
    • Meta-Learning Selection, which adapts selection patterns across task distributions.
  • ...
• Counter-Example(s):
  • Fixed Template, which uses predetermined examples without selection process.
  • Exhaustive Inclusion, which includes all examples without strategic selection.
  • Manual Curation, which relies on human judgment rather than algorithmic selection.
• See: LLM In-Context Learning System, Few-Shot Learning, Example Selection Algorithm, Automated Few-Shot Example Discovery Process, Retrieval System, LLM Prompt Engineering System, Similarity Metric, Diversity Measure.

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