2 citations · 3 across the 6 of their papers we have counts for
6 papers
Useless but Safe? Benchmarking Utility Recovery with User Intent Clarification in Multi-Turn Conversations
Mingqian Zheng, Malia Morgan, Liwei Jiang +2
Current LLM safety alignment techniques improve model robustness against adversarial attacks, but overlook whether and how LLMs can recover helpfulness when benign users clarify th…
Pluralistic Behavior Suite: Stress-Testing Multi-Turn Adherence to Custom Behavioral Policies
Prasoon Varshney, Makesh Narsimhan Sreedhar, Liwei Jiang +2
Large language models (LLMs) are typically aligned to a universal set of safety and usage principles intended for broad public acceptability. Yet, real-world applications of LLMs o…
Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
Liwei Jiang, Yuanjun Chai, Margaret Li +7
Language models (LMs) often struggle to generate diverse, human-like creative content, raising concerns about the long-term homogenization of human thought through repeated exposur…
PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages
Priyanshu Kumar, Devansh Jain, Akhila Yerukola +4
Truly multilingual safety moderation efforts for Large Language Models (LLMs) have been hindered by a narrow focus on a small set of languages (e.g., English, Chinese) as well as a…
To Err is AI : A Case Study Informing LLM Flaw Reporting Practices
Sean McGregor, Allyson Ettinger, Nick Judd +10
In August of 2024, 495 hackers generated evaluations in an open-ended bug bounty targeting the Open Language Model (OLMo) from The Allen Institute for AI. A vendor panel staffed by…
SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behavior
Jing-Jing Li, Valentina Pyatkin, Max Kleiman-Weiner +7
The ideal AI safety moderation system would be both structurally interpretable (so its decisions can be reliably explained) and steerable (to align to safety standards and reflect…