Summary of R-judge: Benchmarking Safety Risk Awareness For Llm Agents, by Tongxin Yuan et al.
R-Judge: Benchmarking Safety Risk Awareness for LLM Agents
by Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu
First submitted to arxiv on: 18 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 tackles a crucial aspect of large language models (LLMs) – their safety in interactive environments. Unlike previous studies focusing on the harmlessness of LLM-generated content, this work prioritizes benchmarking the behavioral safety of these agents within diverse scenarios. The authors introduce R-Judge, a comprehensive benchmark containing 569 records of multi-turn agent interactions across 27 risk scenarios and 5 application categories. They evaluate 11 LLMs on R-Judge, revealing significant room for improvement in risk awareness – even the best-performing model, GPT-4o, achieves only 74.42%. The study highlights that risk awareness is a multidimensional capability involving knowledge and reasoning, making it challenging for LLMs to excel. Fine-tuning on safety judgment can improve model performance, whereas straightforward prompting mechanisms fail. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re playing with a super smart AI chatbot that can help you with lots of tasks. But what if the chatbot doesn’t understand when something is wrong or unsafe? This paper tries to figure out how well these AI chatbots can spot potential dangers in different situations. The researchers created a special test called R-Judge, which has 569 scenarios where the chatbot needs to decide if something is safe or not. They tested 11 different AI models and found that most of them didn’t do very well – even the best one only got it right 74% of the time! This shows how important it is for these AI systems to learn about safety and understanding. |
Keywords
* Artificial intelligence * Fine tuning * Gpt * Prompting
