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.
MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting
A novel framework called MultiTool-CoT uses chain-of-thought prompting to integrate external tools, enhancing LLM performance on datasets requiring numerical reasoning and domain-specific knowledge.
- Year
- 2023
- Venue
- arXiv 2023
- Authors
- 4
- Hosting
- Abstract onlyARXIV-DEFAULT
Cite
Notes
Only stored in your browser.
Attribution
- Abstract & full text
- arxiv.org/abs/2305.16896ARXIV-DEFAULT
- TL;DR
- Semantic Scholar