Abstract
Despite a widespread success in various applications, large language models (LLMs) often stumble when tackling basic physical reasoning or executing robotics tasks, due to a lack of direct experience with the physical nuances of the real world. To address these issues, we propose a Grounding Large language model with Imperfect world MOdel (GLIMO), which utilizes proxy world models such as simulators to collect and synthesize trining data. GLIMO incorporates an LLM agent-based data generator to automatically create high-quality and diverse instruction datasets. The generator includes an iterative self-refining module for temporally consistent experience sampling, a diverse set of question-answering instruction seeds, and a retrieval-augmented generation module for reflecting on prior experiences. Comprehensive experiments show that our approach improve the performance of strong open-source LLMs like LLaMA-3 with a performance boost of 2.04 $\times$, 1.54 $\times$, and 1.82 $\times$ across three different benchmarks, respectively. The performance is able to compete with or surpass their larger counterparts such as GPT-4.
Abstract (translated)
尽管在各种应用中取得了广泛的成功,大型语言模型(LLMs)在处理基本的物理推理或执行机器人任务时常常会陷入困境,因为它们缺乏与现实世界物理细微差别的第一手经验。为解决这些问题,我们提出了一个基于代理世界模型的接地大型语言模型(GLIMO),该模型利用模拟器等代理世界模型收集和合成训练数据。GLIMO包括一个基于LLM的代理程序数据生成器,用于自动创建高质量和多样化的指令数据集。生成器包括一个迭代自校正的时序一致性经验采样模块、一个多样的问题回答指令种子集和一个反映先验经验的检索增强生成模块。 全面的实验证明,我们的方法在三个不同的基准测试中分别将LLM-3的性能提高了2.04倍、1.54倍和1.82倍。性能能够与或超过其较大 counterparts(如 GPT-4)竞争,甚至有些超过它们。
URL
https://arxiv.org/abs/2410.02742