Cultural evolution in populations of Large Language Models
作者: Jérémy Perez, Corentin Léger, Marcela Ovando-Tellez, Chris Foulon, Joan Dussauld, Pierre-Yves Oudeyer, Clément Moulin-Frier
分类: cs.MA, cs.AI, q-bio.PE
发布日期: 2024-03-13
备注: 17 pages, 20 figures. Open-source code available at https://github.com/jeremyperez2/LLM-Culture
💡 一句话要点
提出利用大型语言模型模拟文化演化以解决现有模型不足问题
🎯 匹配领域: 支柱一：机器人控制 (Robot Control) 支柱九：具身大模型 (Embodied Foundation Models)
📋 核心要点
- 现有的代理基础模型在捕捉社会信息转化及其对文化演化的影响方面存在不足。
- 论文提出利用大型语言模型模拟人类文化动态，填补现有模型的空白。
- 开发的开源软件框架允许操控多个文化演化变量，促进相关领域的研究。
- method_zh
📝 摘要（中文）
文化演化研究旨在提供文化随时间变化的因果解释。尽管计算模型在生成可测试假设方面取得了成功，但某些现象仍难以通过现有的代理基础和形式模型捕捉。本文提出利用大型语言模型（LLMs）模拟人类行为，以填补这一空白。我们开发了一个开源框架，允许操控文化演化中的重要变量，如网络结构、个性以及社会信息的聚合和转化方式。该软件具有直观的用户界面，旨在促进文化演化与生成性人工智能领域之间的联系。
🖼️ 关键图片
📄 摘要（原文）
Research in cultural evolution aims at providing causal explanations for the change of culture over time. Over the past decades, this field has generated an important body of knowledge, using experimental, historical, and computational methods. While computational models have been very successful at generating testable hypotheses about the effects of several factors, such as population structure or transmission biases, some phenomena have so far been more complex to capture using agent-based and formal models. This is in particular the case for the effect of the transformations of social information induced by evolved cognitive mechanisms. We here propose that leveraging the capacity of Large Language Models (LLMs) to mimic human behavior may be fruitful to address this gap. On top of being an useful approximation of human cultural dynamics, multi-agents models featuring generative agents are also important to study for their own sake. Indeed, as artificial agents are bound to participate more and more to the evolution of culture, it is crucial to better understand the dynamics of machine-generated cultural evolution. We here present a framework for simulating cultural evolution in populations of LLMs, allowing the manipulation of variables known to be important in cultural evolution, such as network structure, personality, and the way social information is aggregated and transformed. The software we developed for conducting these simulations is open-source and features an intuitive user-interface, which we hope will help to build bridges between the fields of cultural evolution and generative artificial intelligence.