by xingyaoww · Agent Tool · ★ 133
MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback Official Repo for paper MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback by Xingyao Wang\, Zihan Wang\, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng and Heng Ji. MINT benchmark aims to evaluate LLMs' ability to solve tasks with multi-turn interactions by (1) using tools and (2) leveraging natural language feedback. :trophy: Please visit our website for the leaderboard. :warning: WARNING: Evaluation of LLMs requires executing untrusted model-generated code.
| Stars | 133 |
| Forks | 8 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 67.1695507218813/100 |
| Last Updated | 2024-06-04 |
| Created | 2023-09-18 |
| Platforms | python |
| Est. Tokens | ~4821k |
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mint-bench is Official Repo for ICLR 2024 paper MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback by Xingyao Wang*, Zihan Wang*, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng and Hen. It is categorized as a Agent Tool with 133 GitHub stars.
mint-bench is primarily written in Python.
You can find installation instructions and usage details in the mint-bench GitHub repository at github.com/xingyaoww/mint-bench. The project has 133 stars and 8 forks, indicating an active community.
mint-bench is released under the Apache-2.0 license, making it free to use and modify according to the license terms.