Summary of Tooleyes: Fine-grained Evaluation For Tool Learning Capabilities Of Large Language Models in Real-world Scenarios, by Junjie Ye et al.
ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios
by Junjie Ye, Guanyu Li, Songyang Gao, Caishuang Huang, Yilong Wu, Sixian Li, Xiaoran Fan, Shihan Dou, Tao Ji, Qi Zhang, Tao Gui, Xuanjing Huang
First submitted to arxiv on: 1 Jan 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 new evaluation system called ToolEyes, designed to assess the tool learning capabilities of large language models (LLMs) in authentic scenarios. The authors argue that existing evaluations focus on pre-determined outcomes and neglect the complex capabilities required for LLMs to effectively use tools. To address this issue, ToolEyes examines seven real-world scenarios across five dimensions: format alignment, intent comprehension, behavior planning, tool selection, and answer organization. The system also incorporates a library of approximately 600 tools, serving as an intermediary between LLMs and the physical world. Evaluations involving ten LLMs across three categories reveal that LLMs have limited cognitive abilities in tool learning and prefer specific scenarios. Interestingly, increasing model size even exacerbates the hindrance to tool learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can learn new tools, like a hammer or a pencil, just like humans do. Right now, we don’t have a good way to test if a computer is really learning these tools. The researchers created a new system called ToolEyes that can evaluate how well a computer learns tools in real-life situations. They tested this system with 10 different computers and found out some surprising things – for example, some computers are better at using certain tools than others, and even bigger computers aren’t always better at learning tools. |
Keywords
» Artificial intelligence » Alignment
