This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially complex task into simpler subtasks while the latter consists in calling external modules such as a code interpreter. LMs can leverage these augmentations separately or in combination via heuristics, or learn to do so from demonstrations. While adhering to a standard missing tokens prediction objective, such augmented LMs can use various, possibly non-parametric external modules to expand their context processing ability, thus departing from the pure language modeling paradigm. We therefore refer to them as Augmented Language Models (ALMs). The missing token objective allows ALMs to learn to reason, use tools, and even act, while still performing standard natural language tasks and even outperforming most regular LMs on several benchmarks. In this work, after reviewing current advance in ALMs, we conclude that this new research direction has the potential to address common limitations of traditional LMs such as interpretability, consistency, and scalability issues.

翻译：本综述回顾了在语言模型（LMs）中增强推理能力与工具使用能力的研究工作。前者指将潜在复杂任务分解为更简单的子任务，后者则涉及调用代码解释器等外部模块。语言模型可通过启发式方法单独或组合运用这些增强功能，也可从示范数据中学习此类技能。在遵循标准缺失词元预测目标的同时，这类增强型语言模型能够借助各类可能的非参数外部模块扩展其上下文处理能力，从而脱离纯语言建模范式。我们因此将其称为增强型语言模型（ALMs）。缺失词元预测目标使ALMs能够学习推理、使用工具甚至执行动作，同时在标准自然语言任务中保持竞争力，并在多个基准测试中超越多数常规语言模型。通过梳理当前ALMs的研究进展，我们得出结论：这一新兴研究方向有望突破传统语言模型在可解释性、一致性与可扩展性等方面的常见局限。