Summary of Toolsword: Unveiling Safety Issues Of Large Language Models in Tool Learning Across Three Stages, by Junjie Ye et al.
ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages
by Junjie Ye, Sixian Li, Guanyu Li, Caishuang Huang, Songyang Gao, Yilong Wu, Qi Zhang, Tao Gui, Xuanjing Huang
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 ToolSword, a framework that investigates the safety concerns of deploying large language models (LLMs) in real-world scenarios through tool learning. The authors identify six safety scenarios, including malicious queries, jailbreak attacks, noisy misdirection, risky cues, harmful feedback, and error conflicts. Experiments on 11 LLMs show that even GPT-4 is susceptible to these challenges, highlighting the need for further research on tool learning safety. By leveraging ToolSword, researchers can develop safer and more reliable AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make sure computers are safe when we teach them new tricks using big language models. It shows that even really smart models like GPT-4 can be tricked or do bad things if we’re not careful. The authors identify some common problems, like giving the model bad instructions or letting it learn from bad examples. They also provide a special tool to help researchers make better AI systems. |
Keywords
» Artificial intelligence » Gpt
