Automated Few-Shot Example Discovery Process

A Automated Few-Shot Example Discovery Process is a prompt optimization process that automatically finds optimal demonstrations via clustering algorithms and similarity metrics.

• AKA: Automatic Few-Shot Selection Process, Few-Shot Example Mining Process, Demonstration Discovery Process, Example Selection Automation Process.
• Context:
  • It can typically identify representative examples through clustering analysis of training datasets.
  • It can typically select diverse demonstrations maximizing coverage of input space.
  • It can typically rank example candidates using relevance scores based on task objectives.
  • It can typically balance example diversity with task relevance for optimal few-shot sets.
  • It can often implement dynamic selection adapting examples to specific querys.
  • It can often utilize semantic similarity measuring embedding distances between examples.
  • It can often apply information gain metrics selecting most informative examples.
  • It can often reduce manual annotation effort significantly while maintaining task performance.
  • It can range from being a Static Few-Shot Discovery Process to being a Dynamic Few-Shot Discovery Process, depending on its adaptation capability.
  • It can range from being a Random Few-Shot Discovery Process to being a Intelligent Few-Shot Discovery Process, depending on its selection strategy.
  • It can range from being a Single-Criterion Few-Shot Discovery Process to being a Multi-Criterion Few-Shot Discovery Process, depending on its evaluation metrics.
  • It can range from being a Offline Few-Shot Discovery Process to being a Online Few-Shot Discovery Process, depending on its execution timing.
  • ...
• Examples:
  • Clustering-Based Discovery Processes, such as:
    • K-Means Few-Shot Selection using centroid examples.
    • Hierarchical Clustering Example Discovery with dendrogram analysis.
    • DBSCAN Example Mining for density-based selection.
  • Similarity-Based Discovery Processes, such as:
    • Cosine Similarity Example Selection using embedding space.
    • Semantic Matching Discovery with language model embeddings.
  • Optimization-Based Discovery, such as:
    • Greedy Example Selection maximizing marginal gain.
    • Evolutionary Example Discovery using genetic algorithms.
  • ...
• Counter-Examples:
  • Manual Example Selection, which requires human curation.
  • Random Example Sampling, which lacks intelligent selection.
  • Fixed Template Examples, which don't adapt to task requirements.
  • Exhaustive Example Enumeration, which doesn't optimize for efficiency.
• See: Few-Shot Learning, Example Selection Algorithm, Clustering Algorithm, Similarity Metric, DSPy Framework, BootstrapFewShot Optimizer, Information Gain, Demonstration Selection, Prompt Optimization Task, Active Learning.

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