35 citations · 145 across the 18 of their papers we have counts for
4 papers · 1 filter
Async Control: Stress-testing Asynchronous Control Measures for LLM Agents
Asa Cooper Stickland, Jan Michelfeit, Arathi Mani +6
LLM-based software engineering agents are increasingly used in real-world development tasks, often with access to sensitive data or security-critical codebases. Such agents could i…
Why Do Language Model Agents Whistleblow?
Kushal Agrawal, Frank Xiao, Guido Bergman +1
The deployment of Large Language Models (LLMs) as tool-using agents causes their alignment training to manifest in new ways. Recent work finds that language models can use tools in…
Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
Abhay Sheshadri, Aidan Ewart, Phillip Guo +8
Large language models (LLMs) can often be made to behave in undesirable ways that they are explicitly fine-tuned not to. For example, the LLM red-teaming literature has produced a…
Diverse Ensembles Improve Calibration
Asa Cooper Stickland, Iain Murray
Modern deep neural networks can produce badly calibrated predictions, especially when train and test distributions are mismatched. Training an ensemble of models and averaging thei…