Summary of Exploring Critical Testing Scenarios For Decision-making Policies: An Llm Approach, by Weichao Xu et al.
Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach
by Weichao Xu, Huaxin Pei, Jingxuan Yang, Yuchen Shi, Yi Zhang, Qianchuan Zhao
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research proposes an adaptable online testing framework driven by Large Language Models (LLMs) to explore critical and diverse testing scenarios for decision-making policies. The “generate-test-feedback” pipeline leverages templated prompt engineering, harnessing LLMs’ world knowledge and reasoning abilities. A multi-scale scenario generation strategy addresses limitations in fine-grained adjustments, enhancing testing efficiency. Evaluation on five benchmarks shows the proposed method outperforms baselines in uncovering critical and diverse scenarios. This suggests significant promise for advancing decision-making policy testing using LLM-driven methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to test decision-making policies used in areas like autonomous driving and robotics. These policies need to be reliable, but current testing methods are limited because they don’t consider many different scenarios. The researchers design an online testing framework that uses Large Language Models (LLMs) to generate critical and diverse testing scenarios. They also develop a pipeline that generates prompts for the LLMs to test these scenarios. The results show that this method is more effective than previous methods in finding both critical and diverse testing scenarios. |
Keywords
» Artificial intelligence » Prompt
