Summary of Wtu-eval: a Whether-or-not Tool Usage Evaluation Benchmark For Large Language Models, by Kangyun Ning et al.
WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models
by Kangyun Ning, Yisong Su, Xueqiang Lv, Yuanzhe Zhang, Jian Liu, Kang Liu, Jinan Xu
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to exploring the capabilities of Large Language Models (LLMs) in using tools flexibly, as opposed to current research which often assumes mandatory tool use. The authors introduce the Whether-or-not Tool Usage Evaluation (WTU-Eval) benchmark, comprising 11 datasets, six of which require tool usage and five general datasets. LLMs are prompted to use tools according to their needs, revealing that they frequently struggle to determine tool use in general datasets but improve when their ability is similar to ChatGPT. The study highlights the importance of correct tool usage, as incorrect usage impairs performance by 16.8%. To mitigate this, the authors develop a finetuning dataset to enhance tool decision-making, resulting in a 14% average performance improvement and a decrease in incorrect tool usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how big language models can learn to use tools correctly. These models are very good at understanding human language, but they still need help from other tools to get even better. The researchers created a special test to see if the models could figure out when to use these tools and when not to. They found that the models often struggled with this, especially in certain situations. But when they got some extra training, they became much better at using the right tool for the job. |
