Abstract
Language models trained on internet-scale data sets have shown an impressive ability to solve problems in Natural Language Processing and Computer Vision. However, experience is showing that these models are frequently brittle in unexpected ways, and require significant scaffolding to ensure that they operate correctly in the larger systems that comprise "language-model agents." In this paper, we argue that behavior trees provide a unifying framework for combining language models with classical AI and traditional programming. We introduce Dendron, a Python library for programming language model agents using behavior trees. We demonstrate the approach embodied by Dendron in three case studies: building a chat agent, a camera-based infrastructure inspection agent for use on a mobile robot or vehicle, and an agent that has been built to satisfy safety constraints that it did not receive through instruction tuning or RLHF.
Abstract (translated)
在互联网规模数据集上训练的语言模型展现出在自然语言处理和计算机视觉问题中解决问题的令人印象深刻的能力。然而，经验表明，这些模型往往会在意外的方式中变得脆弱，需要显著的支架来确保它们在大型包含 "语言模型代理" 的系统中正常运行。在本文中，我们认为行为树提供了一个统一框架，将语言模型与经典人工智能和传统编程相结合。我们介绍了 Dendron，一个使用行为树的 Python 库来编写语言模型代理。我们通过三个实证研究展示了 Dendron 所代表的方案：构建聊天机器人、用于移动机器人或车辆的相机基础设施检查代理和一种已通过指令调整或 RLHF 满足安全约束的代理。
URL
https://arxiv.org/abs/2404.07439
