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MIPRO / Multi-Stage DSPy (2024)

Free

Optimizes instructions and demonstrations across multi-stage LM programs

FreeFree tier
Type
Open Source
Visit Website GitHub (40 stars)

About MIPRO / Multi-Stage DSPy (2024)

MIPRO (Multi-Stage Instruction and Demonstration Optimizer) is an algorithm for optimizing prompts in multi-stage Language Model (LM) programs. LM programs are pipelines of modular LM calls that increasingly tackle complex NLP tasks, but require carefully crafted prompts for all modules. MIPRO introduces strategies to jointly optimize free-form instructions and few-shot demonstrations across modules without module-level labels or gradients. Its techniques include program- and data-aware instruction proposal, a stochastic mini-batch evaluation function to learn a surrogate objective, and a meta-optimization procedure that refines how LMs construct proposals over time. In benchmarks, MIPRO outperformed baseline optimizers on five of seven diverse multi-stage LM programs using Llama-3-8B, achieving up to 13% accuracy improvement. The algorithm is released as part of the DSPy open-source framework.

Key Features

Joint optimization of instructions and few-shot demonstrations for all modules in an LM program
Program- and data-aware techniques for proposing effective instructions
Stochastic mini-batch evaluation function that learns a surrogate model of the objective
Meta-optimization procedure that refines how LMs construct proposals over time
No need for module-level labels or gradients; works with downstream metrics only
Open-source implementation integrated into the DSPy framework

Pros & Cons

Pros
  • Outperforms baseline optimizers on 5 out of 7 diverse multi-stage LM programs
  • Up to 13% accuracy improvement using Llama-3-8B
  • Does not require module-level labels or gradient access
  • Published research with full paper and code available
  • Open source and integrated into the popular DSPy framework
Cons
  • Currently only implemented within the DSPy ecosystem, limiting standalone use
  • Designed for multi-stage programs; single-step prompt optimization may not benefit
  • Performance depends on the underlying language model (demonstrated with Llama-3-8B)

Best For

Optimizing prompts for multi-stage question-answering pipelinesImproving accuracy of complex reasoning chains in language model programsAutomatically tuning few-shot examples and instructions for modular NLP systemsBenchmarking and advancing prompt optimization research

FAQ

What is MIPRO?
MIPRO (Multi-Stage Instruction and Demonstration Optimizer) is an algorithm that optimizes instructions and few-shot demonstrations across all modules of a multi-stage language model program, maximizing a downstream metric without module-level labels or gradients.
How does MIPRO differ from other prompt optimization methods?
MIPRO jointly optimizes both free-form instructions and few-shot demonstrations for every module in a pipeline, using program- and data-aware proposal techniques, a stochastic surrogate model, and meta-optimization to refine proposals over time.
Is MIPRO available for use?
Yes, MIPRO is released as part of the open-source DSPy framework. The code and benchmark are available on GitHub.
What baseline optimizers does MIPRO outperform?
In the paper, MIPRO outperformed baseline optimizers on five of seven diverse multi-stage LM programs, achieving up to 13% absolute accuracy improvement using Llama-3-8B.
Does MIPRO require access to module-level labels or gradients?
No, MIPRO only requires a downstream metric and access to the LM programs. It does not need module-level labels or gradients, making it applicable to black-box modules.