Transformer based language models (LMs) demonstrate increasing performance
with scale across a wide variety of tasks. Scale alone however cannot enable
models to solve tasks that require access to ephemeral, changing, or private
data that was unavailable at training time. Many useful tasks may also benefit
from LMs being able to access APIs that read or modify state. In this work, we
present Tool Augmented Language Models (TALM), combining a text-only approach
to augment language models with non-differentiable tools, and an iterative
"self-play" technique to bootstrap performance starting from few tool
demonstrations. TALM exhibits strong performance on both a knowledge-heavy QA
task and a reasoning oriented math task with simple tools. At a given model
scale, TALM significantly outperforms non-augmented LMs. We further demonstrate
that TALM successfully performs out-of-distribution inferences on both QA and
math tasks, where non-augmented LMs fail. Our results suggest that Tool
Augmented Language Models are a promising direction to enrich LMs'
capabilities, with less dependence on scale.

本文介绍了一种基于迭代 “自我对弈” 技术的文本增强语言模型方法，使用不可微分的工具扩充语言模型功能，成功在知识丰富型问答和简单工具所需的数学任务中具有很强的表现力，优于非增强型语言模型，在 QA 和数学任务的超越分布推理方面更是取得了成功，证明了工具增强型语言模型是一种非常有前景的方法，可以使语言模型在不依赖于模型（尺度）的基础上具备更多的能力。