LLM Guided Evolution -- The Automation of Models Advancing Models
Clint Morris Michael Jurado Jason Zutty
In the realm of machine learning, traditional model development and automated
approaches like AutoML typically rely on layers of abstraction, such as
tree-based or Cartesian genetic programming. Our study introduces "Guided
Evolution" (GE), a novel framework that diverges from these methods by
utilizing Large Language Models (LLMs) to directly modify code. GE leverages
LLMs for a more intelligent, supervised evolutionary process, guiding mutations
and crossovers. Our unique "Evolution of Thought" (EoT) technique further
enhances GE by enabling LLMs to reflect on and learn from the outcomes of
previous mutations. This results in a self-sustaining feedback loop that
augments decision-making in model evolution. GE maintains genetic diversity,
crucial for evolutionary algorithms, by leveraging LLMs' capability to generate
diverse responses from expertly crafted prompts and modulate model temperature.
This not only accelerates the evolution process but also injects expert like
creativity and insight into the process. Our application of GE in evolving the
ExquisiteNetV2 model demonstrates its efficacy: the LLM-driven GE autonomously
produced variants with improved accuracy, increasing from 92.52% to 93.34%,
without compromising model compactness. This underscores the potential of LLMs
to accelerate the traditional model design pipeline, enabling models to
autonomously evolve and enhance their own designs.