论文标题
TALM：工具增强语言模型
TALM: Tool Augmented Language Models
论文作者
论文摘要
基于变压器的语言模型（LMS）表明，在各种任务中，随着规模的表现提高。但是，单独的比例无法使模型能够解决需要访问培训时间无法访问短暂，更改或私人数据的任务。 LMS能够访问读取或修改状态的API也可能受益许多有用的任务。在这项工作中，我们介绍了工具增强语言模型（TALM），将仅文本方法与非差异性工具相结合，以及一种迭代的“自我播放”技术，以从几个工具演示开始。 TALM在知识繁重的QA任务和使用简单工具的以推理为导向的数学任务上表现出很强的性能。在给定的模型量表上，TALM的表现明显优于非增强的LMS。我们进一步证明，TALM成功地对质量检查和数学任务进行了分发性的推断，而该任务均未实现。我们的结果表明，工具增强语言模型是丰富LMS功能的有前途的方向，并且对规模的依赖程度较小。
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.
