Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
Dohan and David R
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UNVERDICTED 5representative citing papers
EvoPrompt uses LLMs to run evolutionary operators on populations of prompts, outperforming human-engineered prompts by up to 25% on BIG-Bench Hard tasks across 31 datasets.
Large language models can optimize by being prompted with histories of past solutions and scores to propose better ones, producing prompts that raise accuracy up to 8% on GSM8K and 50% on Big-Bench Hard over human-designed baselines.
DEI shows a heterogeneous four-LLM ensemble achieving 124% higher QD-Score and 28% higher coverage than single-model baselines on Core War at equal compute budget.
TacEvo is an LLM-driven self-evolving search method that discovers neural architectures for robotic tactile force regression and grating classification, reporting fitness gains of 56.1% and 96.1% over 20 generations.
citing papers explorer
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Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
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EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers
EvoPrompt uses LLMs to run evolutionary operators on populations of prompts, outperforming human-engineered prompts by up to 25% on BIG-Bench Hard tasks across 31 datasets.
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Large Language Models as Optimizers
Large language models can optimize by being prompted with histories of past solutions and scores to propose better ones, producing prompts that raise accuracy up to 8% on GSM8K and 50% on Big-Bench Hard over human-designed baselines.
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DEI: Diversity in Evolutionary Inference for Quality-Diversity Search
DEI shows a heterogeneous four-LLM ensemble achieving 124% higher QD-Score and 28% higher coverage than single-model baselines on Core War at equal compute budget.
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TacEvo: Self-Evolving Architecture Discovery for Robotic Tactile Perception via LLM-Driven Quality-Diversity Search
TacEvo is an LLM-driven self-evolving search method that discovers neural architectures for robotic tactile force regression and grating classification, reporting fitness gains of 56.1% and 96.1% over 20 generations.