4 papers
Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors
Chen Yueh-Han, Nitish Joshi, Yulin Chen +3
Current LLM safety defenses fail under decomposition attacks, where a malicious goal is decomposed into benign subtasks that circumvent refusals. The challenge lies in the existing…
Predicting Empirical AI Research Outcomes with Language Models
Jiaxin Wen, Chenglei Si, Yueh-han Chen +2
Many promising-looking ideas in AI research fail to deliver, but their validation takes substantial human labor and compute. Predicting an idea's chance of success is thus crucial…
SAGE-Eval: Evaluating LLMs for Systematic Generalizations of Safety Facts
Chen Yueh-Han, Guy Davidson, Brenden M. Lake
Do LLMs robustly generalize critical safety facts to novel situations? Lacking this ability is dangerous when users ask naive questions. For instance, "I'm considering packing melo…
Measuring LLM Novelty As The Frontier Of Original And High-Quality Output
Vishakh Padmakumar, Chen Yueh-Han, Jane Pan +2
As large language models (LLMs) are increasingly used for ideation and scientific discovery, it is important to evaluate their ability to generate novel output. Prior work evaluate…