5 citations · 6 across the 7 of their papers we have counts for
9 papers
Alignment Forecasting: Predicting Misalignment From Training Data
Chen Yueh-Han, Bruce W. Lee, Ilia Sucholutsky +1
Training a language model on data with a narrow flaw can sometimes make the model broadly misaligned. Inspecting the data at face value often does not settle whether it will emerge…
Reasoning Models Struggle to Control their Chains of Thought
Chen Yueh-Han, Robert McCarthy, Bruce W. Lee +5
Chain-of-thought (CoT) monitoring is a promising tool for detecting misbehaviors and understanding the motivations of modern reasoning models. However, if models can control what t…
Training Agents to Self-Report Misbehavior
Bruce W. Lee, Chen Yueh-Han, Tomek Korbak
Frontier AI agents may pursue hidden goals while concealing their pursuit from oversight. Alignment training aims to prevent such behavior by reinforcing the correct goals, but ali…
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…
ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities
Ezra Karger, Houtan Bastani, Chen Yueh-Han +4
Forecasts of future events are essential inputs into informed decision-making. Machine learning (ML) systems have the potential to deliver forecasts at scale, but there is no frame…
Approaching Human-Level Forecasting with Language Models
Danny Halawi, Fred Zhang, Chen Yueh-Han +1
Forecasting future events is important for policy and decision making. In this work, we study whether language models (LMs) can forecast at the level of competitive human forecaste…