Optimizing Language Models for Improved NLP Tasks
Challenges in Prompt Engineering
Designing Language Model (LM) Programs requires time-consuming manual prompt engineering, hindering efficiency. Lack of evaluation metrics for individual LM calls complicates optimization.
Approaches to LM Program Optimization
Various approaches like gradient-guided search and evolutionary algorithms have been introduced, but they fall short in addressing the complexities of multi-stage LM programs.
Introducing MIPRO
MIPRO is a robust approach to optimize prompts for LM programs, focusing on maximizing downstream metrics without requiring module-level labels or gradients.
Architecture of MIPRO
MIPRO employs innovative strategies like bootstrapping demonstrations, grounding techniques, and learning to propose to optimize free-form instructions and few-shot demonstrations for each module in the program.
Key Insights from MIPRO Optimization
Optimizing both instructions and few-shot examples yielded the best overall performance across tasks, paving the way for more efficient and powerful multi-stage LM programs.
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