Summary of Erabal: Enhancing Role-playing Agents Through Boundary-aware Learning, by Yihong Tang et al.
ERABAL: Enhancing Role-Playing Agents through Boundary-Aware Learning
by Yihong Tang, Jiao Ou, Che Liu, Fuzheng Zhang, Di Zhang, Kun Gai
First submitted to arxiv on: 23 Sep 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 presents ERABAL, a framework that enhances role-playing agents’ (RPLAs) capabilities through boundary-aware learning. RPLAs struggle with maintaining role-consistency across conversations, particularly when confronted with subtle queries related to character attributes. To address this challenge, ERABAL combines a generation pipeline for role-specific dialogues with alignment training. The framework achieves notable improvements in WikiRoleEval, CharacterEval, and the role-playing subset of MT-Bench compared to generalist baseline models, requiring significantly fewer training dialogues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Role-playing is an emerging application in Human-Computer Interaction (HCI), using large language models to play characters. This paper helps RPLAs talk consistently about their character’s attributes by learning boundaries. It creates a framework called ERABAL that generates role-specific conversations and trains the model to be better at playing its role. |
Keywords
» Artificial intelligence » Alignment
