8 papers · 1 filter
Instance-Level Difficulty: A Missing Perspective in Machine Unlearning
Hammad Rizwan, Mahtab Sarvmaili, Hassan Sajjad +1
Current research on deep machine unlearning primarily focuses on improving or evaluating the overall effectiveness of unlearning methods while overlooking the varying difficulty of…
Evaluating Defences against Unsafe Feedback in RLHF
Domenic Rosati, Giles Edkins, Harsh Raj +5
While there has been progress towards aligning Large Language Models (LLMs) with human values and ensuring safe behaviour at inference time, safety guards can easily be removed whe…
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…
Explaining the role of Intrinsic Dimensionality in Adversarial Training
Enes Altinisik, Safa Messaoud, Husrev Taha Sencar +2
Adversarial Training (AT) impacts different architectures in distinct ways: vision models gain robustness but face reduced generalization, encoder-based models exhibit limited robu…
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…
Data-centric Prediction Explanation via Kernelized Stein Discrepancy
Mahtab Sarvmaili, Hassan Sajjad, Ga Wu
Existing example-based prediction explanation methods often bridge test and training data points through the model's parameters or latent representations. While these methods offer…
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…