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Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability

Authors :
Li, Haonan
Han, Xudong
Zhai, Zenan
Mu, Honglin
Wang, Hao
Zhang, Zhenxuan
Geng, Yilin
Lin, Shom
Wang, Renxi
Shelmanov, Artem
Qi, Xiangyu
Wang, Yuxia
Hong, Donghai
Yuan, Youliang
Chen, Meng
Tu, Haoqin
Koto, Fajri
Kuribayashi, Tatsuki
Zeng, Cong
Bhardwaj, Rishabh
Zhao, Bingchen
Duan, Yawen
Liu, Yi
Alghamdi, Emad A.
Yang, Yaodong
Dong, Yinpeng
Poria, Soujanya
Liu, Pengfei
Liu, Zhengzhong
Ren, Xuguang
Hovy, Eduard
Gurevych, Iryna
Nakov, Preslav
Choudhury, Monojit
Baldwin, Timothy
Publication Year :
2024

Abstract

To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety. Combining a dynamic leaderboard with an interactive LLM arena, Libra-Leaderboard encourages the joint optimization of capability and safety. Unlike traditional approaches that average performance and safety metrics, Libra-Leaderboard uses a distance-to-optimal-score method to calculate the overall rankings. This approach incentivizes models to achieve a balance rather than excelling in one dimension at the expense of some other ones. In the first release, Libra-Leaderboard evaluates 26 mainstream LLMs from 14 leading organizations, identifying critical safety challenges even in state-of-the-art models.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2412.18551
Document Type :
Working Paper