Summary of Mobilesafetybench: Evaluating Safety Of Autonomous Agents in Mobile Device Control, by Juyong Lee et al.
MobileSafetyBench: Evaluating Safety of Autonomous Agents in Mobile Device Control
by Juyong Lee, Dongyoon Hahm, June Suk Choi, W. Bradley Knox, Kimin Lee
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 introduces MobileSafetyBench, a benchmark for evaluating the safety of mobile device-control agents powered by large language models (LLMs). The authors highlight the importance of ensuring these agents’ safe and reliable behavior to prevent undesirable outcomes. They develop a set of tasks simulating daily scenarios and indirect prompt injection attacks to test the robustness of baseline agents based on state-of-the-art LLMs. Results show that these agents often fail to prevent harm, emphasizing the need for continued research to develop more robust safety mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special test to make sure computer helpers (called “agents”) are safe and don’t do bad things when controlling your phone. Right now, there’s no way to measure how well these agents do this, so they made one! They tested different agents and found that most of them didn’t do very well. So, they came up with a new idea to help the agents be safer. |
Keywords
» Artificial intelligence » Prompt
