22 citations · 54 across the 23 of their papers we have counts for
8 papers · 1 filter
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…
Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse
Maojia Song, Shang Hong Sim, Rishabh Bhardwaj +3
LLMs are an integral component of retrieval-augmented generation (RAG) systems. While many studies focus on evaluating the overall quality of end-to-end RAG systems, there is a gap…
Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique
Tej Deep Pala, Vernon Y. H. Toh, Rishabh Bhardwaj +1
In today's era, where large language models (LLMs) are integrated into numerous real-world applications, ensuring their safety and robustness is crucial for responsible AI usage. A…
WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models
Prannaya Gupta, Le Qi Yau, Hao Han Low +8
WalledEval is a comprehensive AI safety testing toolkit designed to evaluate large language models (LLMs). It accommodates a diverse range of models, including both open-weight and…
Ruby Teaming: Improving Quality Diversity Search with Memory for Automated Red Teaming
Vernon Toh Yan Han, Rishabh Bhardwaj, Soujanya Poria
We propose Ruby Teaming, a method that improves on Rainbow Teaming by including a memory cache as its third dimension. The memory dimension provides cues to the mutator to yield be…
DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling
Pala Tej Deep, Rishabh Bhardwaj, Soujanya Poria
With the proliferation of domain-specific models, model merging has emerged as a set of techniques that combine the capabilities of multiple models into one that can multitask with…