1 citations · 1 across the 6 of their papers we have counts for
6 papers
The Singapore Consensus on Global AI Safety Research Priorities
Yoshua Bengio, Tegan Maharaj, Luke Ong +84
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to ensure that AI is safe, i.e., trustworthy,…
InfAlign: Inference-aware language model alignment
Ananth Balashankar, Ziteng Sun, Jonathan Berant +9
Language model alignment is a critical step in training modern generative language models. Alignment targets to improve win rate of a sample from the aligned model against the base…
Automated Adversarial Discovery for Safety Classifiers
Yash Kumar Lal, Preethi Lahoti, Aradhana Sinha +2
Safety classifiers are critical in mitigating toxicity on online forums such as social media and in chatbots. Still, they continue to be vulnerable to emergent, and often innumerab…
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images
Hansa Srinivasan, Candice Schumann, Aradhana Sinha +5
Capturing the diversity of people in images is challenging: recent literature tends to focus on diversifying one or two attributes, requiring expensive attribute labels or building…
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning
Ananth Balashankar, Xiao Ma, Aradhana Sinha +4
As large language models (LLMs) are widely adopted, new safety issues and policies emerge, to which existing safety classifiers do not generalize well. If we have only observed a f…
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks
Aradhana Sinha, Ananth Balashankar, Ahmad Beirami +3
Real-world natural language processing systems need to be robust to human adversaries. Collecting examples of human adversaries for training is an effective but expensive solution.…