ProTeGi (Prompt Optimization with Textual Gradients)

A ProTeGi (Prompt Optimization with Textual Gradients) is a gradient-based prompt optimization technique that generates natural language gradients via mini-batches to criticize and edit prompts.

• AKA: ProTeGi, Prompt Optimization with Textual Gradients, Textual Gradient Prompt Method, Criticism-Based Prompt Optimization.
• Context:
  • It can generate textual gradients through mini-batch criticism and error analysis.
  • It can edit prompts by moving semantically opposite to identified problems.
  • It can utilize beam search to explore multiple edit paths and select best improvements.
  • It can aggregate feedback from multiple examples to compute gradient directions.
  • It can leverage language models as critics to identify prompt weaknesses.
  • It can apply iterative refinement through successive gradient steps.
  • It can incorporate step size control to balance exploration and stability.
  • It can handle discrete optimization through continuous relaxation in semantic space.
  • It can maintain prompt coherence while applying gradient-based updates.
  • It can combine with momentum techniques as in MAPO for faster convergence.
  • ...
  • It can range from being a Single-Batch ProTeGi to being a Multi-Batch ProTeGi, depending on its batch configuration.
  • It can range from being a Shallow ProTeGi to being a Deep ProTeGi, depending on its iteration depth.
  • It can range from being a Conservative ProTeGi to being an Aggressive ProTeGi, depending on its update magnitude.
  • It can range from being a Task-Specific ProTeGi to being a General ProTeGi, depending on its application domain.
  • ...
• Example(s):
  • Standard ProTeGi Implementation, which uses mini-batches of 5-10 examples for gradient estimation.
  • MAPO (Momentum-Aided Prompt Optimization), which extends ProTeGi with momentum terms.
  • ProTeGi for Code Generation, which optimizes programming prompts using test case feedback.
  • ProTeGi for Translation, which improves translation prompts through bilingual criticism.
  • ...
• Counter-Example(s):
  • Random Prompt Editing, which lacks gradient guidance and systematic improvement.
  • Template-Based Prompt Modification, which uses fixed rules rather than gradients.
  • Evolutionary Prompt Mutation, which uses random variation rather than directed gradients.
  • Manual Prompt Refinement, which relies on human intuition rather than computed gradients.
• See: Gradient-Based Prompt Optimization, TextGrad Framework, Textual Gradient, Natural Language Feedback, Beam Search, Mini-Batch Processing, Semantic Opposition, Prompt Editing Technique, Gradient Descent Algorithm.

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