ToolQA: A Dataset for LLM Question Answering with External Tools

TL;DR

ToolQA is a new dataset designed to evaluate large language models' ability to effectively use external tools for question answering, addressing limitations of previous evaluation methods.

Contribution

The paper introduces ToolQA, a scalable, automated dataset with specialized tools to accurately assess LLMs' external tool-use reasoning capabilities.

Findings

Sets a new benchmark for LLM tool-use evaluation

Highlights strengths and weaknesses of current tool-use LLMs

Provides insights for future improvements in LLMs

Abstract

Large Language Models (LLMs) have demonstrated impressive performance in various NLP tasks, but they still suffer from challenges such as hallucination and weak numerical reasoning. To overcome these challenges, external tools can be used to enhance LLMs' question-answering abilities. However, current evaluation methods do not distinguish between questions that can be answered using LLMs' internal knowledge and those that require external information through tool use. To address this issue, we introduce a new dataset called ToolQA, which is designed to faithfully evaluate LLMs' ability to use external tools for question answering. Our development of ToolQA involved a scalable, automated process for dataset curation, along with 13 specialized tools designed for interaction with external knowledge in order to answer questions. Importantly, we strive to minimize the overlap between our benchmark data and LLMs' pre-training data, enabling a more precise evaluation of LLMs' tool-use reasoning abilities. We conducted an in-depth diagnosis of existing tool-use LLMs to highlight their strengths, weaknesses, and potential improvements. Our findings set a new benchmark for evaluating LLMs and suggest new directions for future advancements. Our data and code are freely available to the broader scientific community on GitHub.