Summary of Understanding the Weakness Of Large Language Model Agents Within a Complex Android Environment, by Mingzhe Xing et al.
Understanding the Weakness of Large Language Model Agents within a Complex Android Environment
by Mingzhe Xing, Rongkai Zhang, Hui Xue, Qi Chen, Fan Yang, Zhen Xiao
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
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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 Large language models (LLMs) have revolutionized intelligent agents, enabling them to tackle complex tasks within domain-specific software like browsers and games. However, applying these models to general-purpose systems like operating systems poses significant challenges. LLMs struggle with maintaining an up-to-date understanding of the vast and dynamic action space, delivering accurate responses, and planning for inter-application cooperation. To address this, AndroidArena was designed as a benchmark to evaluate LLM agents on a modern operating system. A scalable and semi-automated method was developed to construct the benchmark. The task evaluation incorporates accurate and adaptive metrics to handle non-unique solutions. Findings show that even state-of-the-art LLM agents struggle in cross-APP scenarios and adhering to specific constraints, highlighting four key capabilities – understanding, reasoning, exploration, and reflection – as primary reasons for failure. A proposed exploration strategy improved success rates by 27%. This work provides valuable insights into the fine-grained weaknesses of LLM agents, offering a path forward for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are very smart computers that can help with many tasks. But when we try to use them in everyday programs like operating systems, they have some big problems. They struggle to keep up with all the possible actions, give good answers, and work well with other programs. To test how well these LLMs do, we created something called AndroidArena. We also made a special way to make the benchmark (the tests) that can be used many times without needing lots of people to help. When we did the tests, we found out that even the very best LLMs have trouble working with different programs and following rules. This shows us that these smart computers need four important skills: understanding what’s going on, being able to reason, exploring new ideas, and reflecting on their actions. |
