6 citations · 9 across the 17 of their papers we have counts for
10 papers · 1 filter
Likert or Not: LLM Absolute Relevance Judgments on Fine-Grained Ordinal Scales
Charles Godfrey, Ping Nie, Natalia Ostapuk +3
Large language models (LLMs) obtain state of the art zero shot relevance ranking performance on a variety of information retrieval tasks. The two most common prompts to elicit LLM…
Understanding the Inner Workings of Language Models Through Representation Dissimilarity
Davis Brown, Charles Godfrey, Nicholas Konz +2
As language models are applied to an increasing number of real-world applications, understanding their inner workings has become an important issue in model trust, interpretability…
Attributing Learned Concepts in Neural Networks to Training Data
Nicholas Konz, Charles Godfrey, Madelyn Shapiro +3
By now there is substantial evidence that deep learning models learn certain human-interpretable features as part of their internal representations of data. As having the right (or…
How many dimensions are required to find an adversarial example?
Charles Godfrey, Henry Kvinge, Elise Bishoff +4
Past work exploring adversarial vulnerability have focused on situations where an adversary can perturb all dimensions of model input. On the other hand, a range of recent works co…
Fast computation of permutation equivariant layers with the partition algebra
Charles Godfrey, Michael G. Rawson, Davis Brown +1
Linear neural network layers that are either equivariant or invariant to permutations of their inputs form core building blocks of modern deep learning architectures. Examples incl…
Edit at your own risk: evaluating the robustness of edited models to distribution shifts
Davis Brown, Charles Godfrey, Cody Nizinski +2
The current trend toward ever-larger models makes standard retraining procedures an ever-more expensive burden. For this reason, there is growing interest in model editing, which e…
Internal Representations of Vision Models Through the Lens of Frames on Data Manifolds
Henry Kvinge, Grayson Jorgenson, Davis Brown +2
While the last five years have seen considerable progress in understanding the internal representations of deep learning models, many questions remain. This is especially true when…
Testing predictions of representation cost theory with CNNs
Charles Godfrey, Elise Bishoff, Myles Mckay +4
It is widely acknowledged that trained convolutional neural networks (CNNs) have different levels of sensitivity to signals of different frequency. In particular, a number of empir…
On the Symmetries of Deep Learning Models and their Internal Representations
Charles Godfrey, Davis Brown, Tegan Emerson +1
Symmetry is a fundamental tool in the exploration of a broad range of complex systems. In machine learning symmetry has been explored in both models and data. In this paper we seek…
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps
Elizabeth Coda, Nico Courts, Colby Wight +6
While it is not generally reflected in the `nice' datasets used for benchmarking machine learning algorithms, the real-world is full of processes that would be best described as ma…