3 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
Inoculation Prompting: Eliciting traits from LLMs during training can suppress them at test-time
Daniel Tan, Anders Woodruff, Niels Warncke +4
Language model finetuning often results in learning undesirable traits in combination with desired ones. To address this, we propose inoculation prompting: modifying finetuning dat…
Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives
Chloe Li, Mary Phuong, Daniel Tan
As AI systems become more capable of complex agentic tasks, they also become more capable of pursuing undesirable objectives and causing harm. Previous work has attempted to catch…
LISA Technical Report: An Agentic Framework for Smart Contract Auditing
Izaiah Sun, Daniel Tan, Andy Deng
We present LISA, an agentic smart contract vulnerability detection framework that combines rule-based and logic-based methods to address a broad spectrum of vulnerabilities in smar…
Emergent misalignment as prompt sensitivity: A research note
Tim Wyse, Twm Stone, Anna Soligo +1
Betley et al. (2025) find that language models finetuned on insecure code become emergently misaligned (EM), giving misaligned responses in broad settings very different from those…
Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models
Jan Wehner, Sahar Abdelnabi, Daniel Tan +2
Representation Engineering (RepE) is a novel paradigm for controlling the behavior of LLMs. Unlike traditional approaches that modify inputs or fine-tune the model, RepE directly m…