30 citations · 67 across the 15 of their papers we have counts for
5 papers · 1 filter
Remote Labor Index: Measuring AI Automation of Remote Work
Mantas Mazeika, Alice Gatti, Cristina Menghini +44
AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To mea…
The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems
Richard Ren, Arunim Agarwal, Mantas Mazeika +13
As large language models (LLMs) become more capable and agentic, the requirement for trust in their outputs grows significantly, yet at the same time concerns have been mounting th…
LLM Defenses Are Not Robust to Multi-Turn Human Jailbreaks Yet
Nathaniel Li, Ziwen Han, Ian Steneker +6
Recent large language model (LLM) defenses have greatly improved models' ability to refuse harmful queries, even when adversarially attacked. However, LLM defenses are primarily ev…
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes
Alessio Mazzetto, Cristina Menghini, Andrew Yuan +2
We develop a rigorous mathematical analysis of zero-shot learning with attributes. In this setting, the goal is to label novel classes with no training data, only detectors for att…
TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data
Wasu Piriyakulkij, Cristina Menghini, Ross Briden +4
Machine learning practitioners often have access to a spectrum of data: labeled data for the target task (which is often limited), unlabeled data, and auxiliary data, the many avai…