Edge 382: Google DeepMind's PrompBreeder Self-Improves Prompts
The method combines chain of thoughts, plan and solve and evolutionary algorithms in a single mthod.
Reasoning and prompt evolution/optimization are being recognized as the next significant frontier for large language models(LLMs). We have all been dazzled by projects like AutoGPT or BabyAGI that constantly evolve prompts to achieve a specific goal in a way that resembles reasoning. Among the various strategies employed to enhance the reasoning capabilities of LLMs, one of the prominent ones is Chain-of-Thought Prompting, often hailed for its effectiveness. However, it’s worth noting that manually crafted prompt strategies tend to fall short of optimal performance.
Recently, researchers from Google DeepMind unveiled PROMPTBREEDER , a self-improving prompt algorithm that uses evolutionary techniques to arrive to the best prompts for a given task. PROMPTBREEDER addresses some of the limitations of CoT with a simple and super clever algorithm that improves the prompts in a self-referential manner.