MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting
This addresses the challenge of enhancing reasoning capabilities in LLMs for tasks requiring numerical and domain-specific knowledge, representing an incremental improvement.
The paper tackled the problem of improving large language models' performance on reasoning tasks by proposing MultiTool-CoT, a framework that uses chain-of-thought prompting to integrate multiple external tools like a calculator and knowledge retriever, achieving state-of-the-art results on the NumGLUE Task 2 dataset.
Large language models (LLMs) have achieved impressive performance on various reasoning tasks. To further improve the performance, we propose MultiTool-CoT, a novel framework that leverages chain-of-thought (CoT) prompting to incorporate multiple external tools, such as a calculator and a knowledge retriever, during the reasoning process. We apply MultiTool-CoT to the Task 2 dataset of NumGLUE, which requires both numerical reasoning and domain-specific knowledge. The experiments show that our method significantly outperforms strong baselines and achieves state-of-the-art performance.