Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt (2412.05967v1)

Abstract: Prompting and fine-tuning have emerged as two competing paradigms for augmenting LLMs 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 LLMs 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 LLMs (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.

• The paper introduces language hooks, a triplet mechanism integrating a program, a trigger, and eligibility criteria to interleave tool outputs with generated text.
• It details implementations for mathematical calculations, knowledge retrieval, and safety guardrails, enhancing modular reasoning across tasks.
• Benchmarking on GSM8K and HotpotQA demonstrates the framework's superior generalizability and adaptability compared to state-of-the-art baselines.

An Analysis of the Language Hooks Framework for Augmenting LLM Reasoning

The paper "Language hooks: a modular framework for augmenting LLM reasoning that decouples tool usage from the model and its prompt" introduces a novel methodology for enhancing LLM (LM) capabilities through the decoupling of external tool usage from the model’s task-specific prompt and the model itself. Unlike prompting or fine-tuning paradigms, language hooks employ an algorithmic framework that interleaves text generation with the execution of modular programs. These programs can invoke external tools, auxiliary LMs, and modify existing contexts conditionally based on emerging contexts and the capabilities available. This paper motivates the introduction of this framework by its modular, task-agnostic, model-agnostic, and non-intrusive approach, aiming to extend the adaptability and efficacy of LLMs in handling diverse tasks.

Key Contributions

1. Framework Introduction: The paper introduces language hooks as a triplet consisting of a program, a trigger, and eligibility criteria. These hooks are executed conditionally between text sentences generated by the base model, potentially modifying the immediate context and incorporating tool outputs seamlessly into the reasoning process.
2. Implementation of Specific Hooks: The paper showcases concrete implementations of hooks designed for three capabilities: mathematical calculations, knowledge retrieval, and guardrail interception. These demonstrate the framework's capacity to handle specific domain challenges efficiently.
3. Benchmarking: The researchers benchmarked their method against state-of-the-art baselines, including CoT, ReAct, PAL, and DSP, across multiple datasets in mathematical reasoning and multi-hop QA tasks. Results highlight the competitive performance of the language hooks approach, specifically noting superior generalizability and adaptability in composite task settings.
4. Future-Oriented Capability: This framework provides a pathway for developing LLMs with event-driven, flexible tool usage, which is diverse and context-sensitive. It underscores a shift towards externally validated model outputs in safety-critical applications.

Numerical and Methodological Insights

By employing benchmarks such as GSM8K and HotpotQA, language hooks have demonstrated performance on par with or surpassing specialized approaches such as PAL and DSP in certain evaluation settings. Specifically, the modular framework supports impressive adaptability across tasks not anticipated during framework design, demonstrating the value of its abstraction in novel composite task benchmarking, growing beyond traditional task-specific training methods.

Implications and Future Directions

The language hooks model represents an evolutionary step towards more sophisticated, tool-integrated LLMs that can transcend the limitations present in hard-coded or prompt-based tool interactions. With its external validation framework, potential future research directions could explore more sophisticated program designs capable of recognizing and addressing biases or inequitable outputs autonomously.

Moreover, there's an opportunity to expand the versatility of language hooks through enhanced programmatic interventions across emerging applications, including safety-critical contexts like content moderation or dynamically evolving data streams. This leap towards greater modularity and seamless external interaction paves the way for more intelligent and contextually aware AI systems.

This research underlines the importance of modularity, flexibility, and agnosticism in model augmentation techniques, advocating for versatile and general methods for future AI systems. It poses a compelling case for continued exploration in the integration of tools and language processing, fostering an ecosystem where AI can make informed, real-time decisions with accountability and transparency.