ART: Automatic multi-step reasoning and tool-use for large language models
This work addresses the need for efficient and scalable reasoning in LLMs for AI researchers and practitioners, though it is incremental by building on prior chain of thought and tool-use methods.
The paper tackles the problem of automating multi-step reasoning and tool-use for large language models (LLMs) by introducing the ART framework, which automatically generates reasoning steps as programs and integrates external tools, achieving substantial improvements over few-shot prompting and automatic chain of thought on unseen tasks in benchmarks like BigBench and MMLU.
Large language models (LLMs) can perform complex reasoning in few- and zero-shot settings by generating intermediate chain of thought (CoT) reasoning steps. Further, each reasoning step can rely on external tools to support computation beyond the core LLM capabilities (e.g. search/running code). Prior work on CoT prompting and tool use typically requires hand-crafting task-specific demonstrations and carefully scripted interleaving of model generations with tool use. We introduce Automatic Reasoning and Tool-use (ART), a framework that uses frozen LLMs to automatically generate intermediate reasoning steps as a program. Given a new task to solve, ART selects demonstrations of multi-step reasoning and tool use from a task library. At test time, ART seamlessly pauses generation whenever external tools are called, and integrates their output before resuming generation. ART achieves a substantial improvement over few-shot prompting and automatic CoT on unseen tasks in the BigBench and MMLU benchmarks, and matches performance of hand-crafted CoT prompts on a majority of these tasks. ART is also extensible, and makes it easy for humans to improve performance by correcting errors in task-specific programs or incorporating new tools, which we demonstrate by drastically improving performance on select tasks with minimal human intervention.