Tool-Augmented Language Models

• Tool Augmented Language Models are LLMs enhanced with external tool interfaces for dynamic data retrieval and multi-step reasoning.
• They employ a methodology that inserts tool invocation tokens into the output stream, enabling precise, real-time computations and API interactions.
• These models extend traditional pretraining capabilities by integrating symbolic and black-box computations, improving accuracy and functionality in diverse applications.

Tool Augmented LLMs (TALM) are LLMs architected to interface with external tools—such as APIs, search engines, code interpreters, solvers, and databases—during inference, thereby extending their capabilities beyond those endowed solely by pretraining data and parametric memory. As a technical paradigm, TALMs integrate symbolic or black-box computation into the generative process, enabling precise retrieval, up-to-date reasoning, and multi-step interaction with the external world.

1. Formal Definitions and Architectural Foundations

A Tool Augmented LLM consists of a pretrained LLM π (e.g., a Transformer), augmented with the ability to produce, within its output stream, tool invocation tokens or API call programs. Each available tool t maps an input signature