Summary of Tell Me What You Don’t Know: Enhancing Refusal Capabilities Of Role-playing Agents Via Representation Space Analysis and Editing, by Wenhao Liu et al.
Tell Me What You Don’t Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing
by Wenhao Liu, Siyu An, Junru Lu, Muling Wu, Tianlong Li, Xiaohua Wang, Xiaoqing Zheng, Di Yin, Xing Sun, Xuanjing Huang
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 A novel evaluation benchmark is developed to assess Role-Playing Agents (RPAs) in recognizing and responding to conflicting queries that challenge their role-play knowledge. The benchmark includes contextual, parametric, and non-conflicting requests to analyze RPAs’ performance under different conflict scenarios. Extensive evaluation reveals significant performance gaps among RPAs when faced with various types of conflicts. A representation-level analysis uncovers the existence of rejection and direct response regions within the model’s forwarding representation, influencing its final response behavior. To address this issue, a lightweight representation editing approach is introduced to shift conflicting requests to the rejection region, enhancing the RPA’s refusal accuracy while maintaining general role-playing capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Role-Playing Agents are smart systems that can pretend to be people or things. Sometimes they struggle with questions that don’t fit their “role.” To see how well these agents do when faced with tricky questions, researchers created a special test. They tested the agents on different types of questions and found that some agents did much better than others. The researchers looked closely at why this was happening and discovered that the agents’ internal workings were influencing their answers. To help the agents do better, they developed a new way to adjust how the agents process information. This helped the agents give more accurate answers when faced with tricky questions while still being able to answer other questions correctly. |
