Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt
This addresses the challenge of efficiently integrating tools into LLMs for broader applications, offering a more flexible and generalizable solution compared to existing methods.
The paper tackles the problem of augmenting LLMs with new capabilities like tool usage by introducing a modular framework called language hooks, which decouples tool usage from both the model and its prompt, and it outperforms task-aware approaches while demonstrating generalization to novel tasks.
Prompting and fine-tuning have emerged as two competing paradigms for augmenting language models with new capabilities, such as the use of tools. Prompting approaches are quick to set up but rely on providing explicit demonstrations of each tool's usage in the model's prompt, thus coupling tool use to the task at hand and limiting generalisation. Fine-tuning removes the need for task-specific demonstrations of tool usage at runtime; however, this ties new capabilities to a single model, thus making already-heavier setup costs a recurring expense. In this paper, we introduce language hooks, a novel framework for augmenting language models with new capabilities that is decoupled both from the model's task-specific prompt and from the model itself. The language hook algorithm interleaves text generation by the base model with the execution of modular programs that trigger conditionally based on the existing text and the available capabilities. Upon triggering, programs may call external tools, auxiliary language models (e.g. using tool specific prompts), and modify the existing context. We benchmark our method against state-of-the-art baselines, find that it outperforms task-aware approaches, and demonstrate its ability to generalise to novel tasks.