Prompt Codebooks recasts automatic prompt optimization as discrete learning over a finite vocabulary of atomic natural-language instincts with per-instance routing, yielding up to +30.36 point gains over zero-shot and shorter prompts on six benchmarks.
InInternational Conference on Machine Learning (ICML)
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Prompt Codebooks: Discrete Compositional Optimization for Language Model Instruction Refinement
Prompt Codebooks recasts automatic prompt optimization as discrete learning over a finite vocabulary of atomic natural-language instincts with per-instance routing, yielding up to +30.36 point gains over zero-shot and shorter prompts on six benchmarks.