3 citations · 3 across the 7 of their papers we have counts for
7 papers
SurakshaEval: An Indic Safety Benchmark for Multilingual LLMs
Debopriyo Banerjee, Kapil Rajesh Kavitha, Angana Borah +11
Existing safety evaluation datasets for large language models (LLMs) predominantly focus on English and Western contexts, often overlooking the linguistic diversity and culturally…
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability
Haonan Li, Xudong Han, Zenan Zhai +32
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 le…
Loki: An Open-Source Tool for Fact Verification
Haonan Li, Xudong Han, Hao Wang +7
We introduce Loki, an open-source tool designed to address the growing problem of misinformation. Loki adopts a human-centered approach, striking a balance between the quality of f…
Arabic Dataset for LLM Safeguard Evaluation
Yasser Ashraf, Yuxia Wang, Bin Gu +2
The growing use of large language models (LLMs) has raised concerns regarding their safety. While many studies have focused on English, the safety of LLMs in Arabic, with its lingu…
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Lizhi Lin, Honglin Mu, Zenan Zhai +9
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed. In li…
A Chinese Dataset for Evaluating the Safeguards in Large Language Models
Yuxia Wang, Zenan Zhai, Haonan Li +6
Many studies have demonstrated that large language models (LLMs) can produce harmful responses, exposing users to unexpected risks when LLMs are deployed. Previous studies have pro…
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Renxi Wang, Haonan Li, Minghao Wu +4
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks. However, the procedure to optimizing the mixing of instruction datas…