6 papers · 1 filter
GAPS: Dimension-Level Gates for Conditional Activation Steering
Moghis Fereidouni, Muhammad Umair Haider, Hassan Sajjad +1
Activation steering suppresses undesired behaviors in language models by adding a steering vector to the hidden state during generation. Recent conditional methods such as CAST and…
Dependency Parsing is More Parameter-Efficient with Normalization
Paolo Gajo, Domenic Rosati, Hassan Sajjad +1
Dependency parsing is the task of inferring natural language structure, often approached by modeling word interactions via attention through biaffine scoring. This mechanism works…
Resolving Lexical Bias in Model Editing
Hammad Rizwan, Domenic Rosati, Ga Wu +1
Model editing aims to modify the outputs of large language models after they are trained. Previous approaches have often involved direct alterations to model weights, which can res…
Representation Noising: A Defence Mechanism Against Harmful Finetuning
Domenic Rosati, Jan Wehner, Kai Williams +7
Releasing open-source large language models (LLMs) presents a dual-use risk since bad actors can easily fine-tune these models for harmful purposes. Even without the open release o…
Long-form evaluation of model editing
Domenic Rosati, Robie Gonzales, Jinkun Chen +6
Evaluations of model editing currently only use the `next few token' completions after a prompt. As a result, the impact of these methods on longer natural language generation is l…
Immunization against harmful fine-tuning attacks
Domenic Rosati, Jan Wehner, Kai Williams +4
Large Language Models (LLMs) are often trained with safety guards intended to prevent harmful text generation. However, such safety training can be removed by fine-tuning the LLM o…