Title: Nature-Inspired Population-Based Evolution of Large Language Models
Primary area: generative models
Keywords: large language models, population-based evolution, genetic algorithm, genetic swarms algorithm

Abstract: Evolution, the engine behind the survival and growth of life on Earth, operates through the population-based process of reproduction. Inspired by this principle, this paper formally defines a newly emerging problem---the population-based evolution of large language models (LLMs)---and introduces a novel framework. Starting with a population of parent LLMs, our framework enables the population to evolve through four key operations: (i) crossover, merging the weights of different parents to create offspring LLMs, (ii) mutation, introducing small, random changes to model weights to foster diversity, (iii) selection, prioritizing high-performing models, and (iv) succession, transferring the learned experience from parent to offspring LLMs. With only 200 samples per new task, the LLM population evolves rapidly to adapt to the task at hand, without any gradients. Experiments on 12 datasets show that our framework consistently outperforms existing multi-LLM merging and adaptation methods, achieving accuracy gains of up to 54.8% over the best LLM in the initial population. Moreover, our framework allows for the evolution of LLMs across multiple new tasks simultaneously, scaling effectively with populations of up to 40 LLMs, and even zero-shot generalization to unseen held-out tasks. We have open-sourced the code on GitHub enabling reproduction of our proposed framework using just a single 4090 GPU with 24GB memory, without any performance degradation.

TL;DR: A new evolutionary framework for optimizing LLMs uses biological principles (crossover, mutation, selection, and succession) to adapt models to new tasks with minimal samples, achieving significant performance improvements over existing methods.