35 citations · 145 across the 18 of their papers we have counts for
8 papers · 1 filter
Does Unlearning Truly Unlearn? A Black Box Evaluation of LLM Unlearning Methods
Jai Doshi, Asa Cooper Stickland
Large language model unlearning aims to remove harmful information that LLMs have learnt to prevent their use for malicious purposes. LLMU and RMU have been proposed as two methods…
Steering Without Side Effects: Improving Post-Deployment Control of Language Models
Asa Cooper Stickland, Alexander Lyzhov, Jacob Pfau +2
Language models (LMs) have been shown to behave unexpectedly post-deployment. For example, new jailbreaks continually arise, allowing model misuse, despite extensive red-teaming an…
The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
Lukas Berglund, Meg Tong, Max Kaufmann +4
We expose a surprising failure of generalization in auto-regressive large language models (LLMs). If a model is trained on a sentence of the form "A is B", it will not automaticall…
Taken out of context: On measuring situational awareness in LLMs
Lukas Berglund, Asa Cooper Stickland, Mikita Balesni +5
We aim to better understand the emergence of `situational awareness' in large language models (LLMs). A model is situationally aware if it's aware that it's a model and can recogni…
When does Parameter-Efficient Transfer Learning Work for Machine Translation?
Ahmet Üstün, Asa Cooper Stickland
Parameter-efficient fine-tuning methods (PEFTs) offer the promise of adapting large pre-trained models while only tuning a small number of parameters. They have been shown to be co…
Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters
Asa Cooper Stickland, Alexandre Bérard, Vassilina Nikoulina
Adapter layers are lightweight, learnable units inserted between transformer layers. Recent work explores using such layers for neural machine translation (NMT), to adapt pre-train…
Deep Transformers with Latent Depth
Xian Li, Asa Cooper Stickland, Yuqing Tang +1
The Transformer model has achieved state-of-the-art performance in many sequence modeling tasks. However, how to leverage model capacity with large or variable depths is still an o…
Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation
Asa Cooper Stickland, Xian Li, Marjan Ghazvininejad
There has been recent success in pre-training on monolingual data and fine-tuning on Machine Translation (MT), but it remains unclear how to best leverage a pre-trained model for a…