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

Damien de Mijolla, Wen Yang, Philippa Duckett, Christopher Frye, Mark Worrall·December 08, 2024

Summary

Language hooks offer a modular framework for augmenting large language models (LLMs) like ChatGPT, enabling external validation and improving performance in tasks such as mathematical reasoning, multi-hop question answering, and composite datasets. This approach outperforms task-aware methods, generalizing to novel tasks and being task-agnostic, model-agnostic, and modular. The algorithm integrates external validation methods with LLMs, featuring retriever, guardrail, and calculator hooks for proposing text rewrites, ensuring best guesses when lacking information, and assisting in mathematical reasoning, respectively. The text evaluates various methods for mathematical reasoning, focusing on CoT, ReAct, PAL, and language hooks, demonstrating their effectiveness in externalizing validation and improving performance across different datasets and tasks. Language hooks are modular, addressing issues of "distractibility" in ReAct and improving performance without distracting the base model, offering external validation that enhances internal safety measures in the base model. The text discusses advancements in language models and their applications in multi-step reasoning, mathematical problem-solving, question answering, and knowledge-intensive tasks, highlighting the development of models that can self-correct, interact with tools, and solve problems through logical reasoning, aiming to enhance the capabilities of language models in various domains.

Key findings

6

Introduction
Background
Overview of large language models (LLMs) like ChatGPT
Importance of augmenting LLMs for specific tasks
Objective
To explore the use of language hooks in augmenting LLMs for improved performance in various tasks
To compare language hooks with task-aware methods in terms of generalization, modularity, and model-agnostic capabilities
Method
Data Collection
Techniques for collecting data relevant to tasks requiring language hooks
Data Preprocessing
Methods for preparing data to be compatible with language hooks and LLMs
Integration of Language Hooks
Description of retriever, guardrail, and calculator hooks
Explanation of how each hook functions within the LLM framework
Integration process and its impact on task performance
Evaluation
Mathematical Reasoning
Overview of methods for evaluating mathematical reasoning capabilities
Comparison of CoT, ReAct, PAL, and language hooks
Analysis of performance improvements across different datasets and tasks
Advancements in Language Models
Multi-step Reasoning
Discussion on advancements in handling multi-step reasoning tasks
Mathematical Problem-solving
Examination of models' capabilities in solving mathematical problems through logical reasoning
Question Answering
Analysis of language models' performance in answering complex questions
Knowledge-intensive Tasks
Overview of models' ability to engage with knowledge-intensive tasks effectively
Applications and Implications
Enhancing Internal Safety Measures
Discussion on how language hooks improve the safety of base models through external validation
Modularity and Distractibility
Analysis of language hooks' role in addressing issues of "distractibility" in ReAct
Future Directions
Speculation on future developments in language models and their applications
Potential for enhancing language models' capabilities in various domains through modular augmentation
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
How do language hooks compare to task-aware methods in terms of generalization and performance across different tasks and datasets?
What are the key features of the retriever, guardrail, and calculator hooks in the context of language hooks?
How do language hooks improve the performance of large language models in tasks such as mathematical reasoning and multi-hop question answering?