19 citations · 62 across the 26 of their papers we have counts for
13 papers · 1 filter
Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models
Malikeh Ehghaghi, Boglárka Ecsedi, Marsha Chechik +1
Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly treating all attacks as equally…
Model Merging via Data-Free Covariance Estimation
Marawan Gamal Abdel Hameed, Derek Tam, Pascal Jr Tikeng Notsawo +2
Model merging provides a way of cheaply combining individual models to produce a model that inherits each individual's capabilities. While some merging methods can approach the per…
The Appeal and Reality of Recycling LoRAs with Adaptive Merging
Haokun Liu, Gyung Hyun Je, Marco Ciccone +3
The widespread availability of fine-tuned LoRA modules for open pre-trained models has led to an interest in methods that can adaptively merge LoRAs to improve performance. These m…
Efficiently Estimating Data Efficiency for Language Model Fine-tuning
Gyung Hyun Je, Colin Raffel
While large language models (LLMs) demonstrate reasonable zero-shot capability across many downstream tasks, fine-tuning is a common practice to improve their performance. However,…
Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator
YuXin Li, Felix Dangel, Derek Tam +1
The diagonal of a model's Fisher Information Matrix (the "Fisher diagonal") has frequently been used as a way to measure parameter sensitivity. Typically, the Fisher diagonal is es…
The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions
Devin Kwok, Gül Sena Altıntaş, Colin Raffel +1
Neural network training is inherently sensitive to initialization and the randomness induced by stochastic gradient descent. However, it is unclear to what extent such effects lead…