TALM: Tool Augmented Language Models (2205.12255v1)

Abstract: Transformer based LLMs (LMs) demonstrate increasing performance with scale across a wide variety of tasks. Scale alone however cannot enable models to solve tasks that require access to ephemeral, changing, or private data that was unavailable at training time. Many useful tasks may also benefit from LMs being able to access APIs that read or modify state. In this work, we present Tool Augmented LLMs (TALM), combining a text-only approach to augment LLMs with non-differentiable tools, and an iterative "self-play" technique to bootstrap performance starting from few tool demonstrations. TALM exhibits strong performance on both a knowledge-heavy QA task and a reasoning oriented math task with simple tools. At a given model scale, TALM significantly outperforms non-augmented LMs. We further demonstrate that TALM successfully performs out-of-distribution inferences on both QA and math tasks, where non-augmented LMs fail. Our results suggest that Tool Augmented LLMs are a promising direction to enrich LMs' capabilities, with less dependence on scale.

Tool Augmented LLMs (TALM): Enhancing Transformer-Based LMs with External Tools

The research paper "TALM: Tool Augmented LLMs" presents a transformative approach for enhancing the effectiveness of Transformer-based LLMs (LMs) by integrating them with external tools. The paper addresses a limitation of LMs in handling tasks requiring access to ephemeral, constantly changing, or private data—parameters inaccessible during the training phase. The authors introduce Tool Augmented LLMs (TALM) which augment LMs with external tools via a text-to-text API interface, employing an iterative "self-play" methodology to refine their performance.

Key Contributions and Methodology

The TALM framework equips LLMs with the ability to invoke external tools and use their outputs to generate more accurate task-specific results, significantly transcending the limitations of traditional scale alone. TALM is notable for a few critical contributions:

1. Text-to-Text API Interface: TALM utilizes a tool interface that allows LLMs to interact with external tools through a straightforward text-based protocol. This interface supports functional compatibility across a wide range of use-cases, enhancing the model's capability using non-differentiable tools.
2. Iterative Self-Play Technique: A self-play strategy is employed to bootstrap the models' tool-utilization efficiency, beginning with few labeled tool-use examples. The approach leverages existing task data, iteratively augmenting the tool-use dataset, which enhances the model's ability to generate tool-invocations and appropriate responses, consequently improving performance metrics on diverse tasks.

Evaluation and Results

The effectiveness of TALM is evaluated across two domains: Natural Questions (NQ) and MathQA, each demonstrating distinct aspects of knowledge reliance and reasoning capabilities.

• Natural Questions (NQ): TALM showcased its capacity for knowledge-driven QA tasks, outperforming traditional LMs of significantly larger scales. The use of a BM25-based retrieval system, simulating an external retrieval tool, allowed TALM to adapt flexibly to changing content, dramatically reducing errors seen in static model outputs.
• MathQA: In a reasoning-intensive domain such as mathematical problem-solving, TALM again displayed superiority over traditional models by employing simple arithmetic tools. The iterative self-play significantly enhanced the model's output quality, evidencing its potential to excel in reasoning without an exhaustive label-dependent training regime.

Implications and Future Directions

This research introduces TALM as a robust framework that mitigates the need for solely scale-dependent performance improvements in LLMs. By integrating external tools, TALMs can dynamically access current, context-sensitive data and perform operations beyond the intrinsic capacity of the model’s parameters alone.

The implications of TALM for further AI development are multifaceted:

• Reduced Dependence on Model Scaling: TALM suggests that significant performance enhancements can be achieved without proportionally increasing model scale, which remains a resource-intensive process.
• Extending Model Utility: The approach exemplifies a paradigm shift whereby LMs can be extended with domain-specific, dynamic tools, potentially facilitating applications in personalized data management and real-time decision-making tasks.
• Future Prospects: The integration of more sophisticated or multi-step tool interactions through advancements in RL or meta-learning could further broaden the applicability of TALM across domains. The scalability of tool applications, coupled with adaptive learning strategies such as iterative self-play, provides a blueprint for future explorations into tool-augmented intelligence systems.

In conclusion, TALM represents an innovative step towards equipping LLMs with the capacity to utilize external information and operations viably, thus laying the groundwork for more adaptable and intelligent AI systems. The work opens new avenues for enhancing LMs while curbing the prohibitive costs associated with their scaling.