Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916245723414528 |
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| author | Wu, Mengsong Zhu, Tong Han, Han Tan, Chuanyuan Zhang, Xiang Chen, Wenliang |
| author_facet | Wu, Mengsong Zhu, Tong Han, Han Tan, Chuanyuan Zhang, Xiang Chen, Wenliang |
| contents | This paper presents a new tool learning dataset Seal-Tools, which contains self-instruct API-like tools. Seal-Tools not only offers a large number of tools, but also includes instances which demonstrate the practical application of tools. Seeking to generate data on a large scale while ensuring reliability, we propose a self-instruct method to generate tools and instances, allowing precise control over the process. Moreover, our Seal-Tools contains hard instances that call multiple tools to complete the job, among which some are nested tool callings. For precise and comprehensive evaluation, we use strict format control and design three metrics from different dimensions. Therefore, Seal-Tools can serve as a new benchmark to evaluate the tool-calling ability of LLMs. Finally, we evaluate several prevalent LLMs and our finetuned model on Seal-Tools. The results show that current systems are far from perfect. The code, data and experiment results are available at https://github.com/fairyshine/Seal-Tools . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08355 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark Wu, Mengsong Zhu, Tong Han, Han Tan, Chuanyuan Zhang, Xiang Chen, Wenliang Computation and Language This paper presents a new tool learning dataset Seal-Tools, which contains self-instruct API-like tools. Seal-Tools not only offers a large number of tools, but also includes instances which demonstrate the practical application of tools. Seeking to generate data on a large scale while ensuring reliability, we propose a self-instruct method to generate tools and instances, allowing precise control over the process. Moreover, our Seal-Tools contains hard instances that call multiple tools to complete the job, among which some are nested tool callings. For precise and comprehensive evaluation, we use strict format control and design three metrics from different dimensions. Therefore, Seal-Tools can serve as a new benchmark to evaluate the tool-calling ability of LLMs. Finally, we evaluate several prevalent LLMs and our finetuned model on Seal-Tools. The results show that current systems are far from perfect. The code, data and experiment results are available at https://github.com/fairyshine/Seal-Tools . |
| title | Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.08355 |