3.7k citations · 12.4k across the 59 of their papers we have counts for
10 papers · 1 filter
Merging by Matching Models in Task Parameter Subspaces
Derek Tam, Mohit Bansal, Colin Raffel
Model merging aims to cheaply combine individual task-specific models into a single multitask model. In this work, we view past merging methods as leveraging different notions of a…
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization
Prateek Yadav, Leshem Choshen, Colin Raffel +1
Parameter-efficient fine-tuning (PEFT) techniques make it possible to efficiently adapt a language model to create "expert" models that specialize to new tasks or domains. Recent t…
Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model
Haikang Deng, Colin Raffel
While large language models have proven effective in a huge range of downstream applications, they often generate text that is problematic or lacks a desired attribute. In this pap…
Uncovering Model Processing Strategies with Non-Negative Per-Example Fisher Factorization
Michael Matena, Colin Raffel
We introduce NPEFF (Non-Negative Per-Example Fisher Factorization), an interpretability method that aims to uncover strategies used by a model to generate its predictions. NPEFF de…
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models
Nikhil Kandpal, Brian Lester, Mohammed Muqeeth +6
Currently, most machine learning models are trained by centralized teams and are rarely updated. In contrast, open-source software development involves the iterative development of…
Soft Merging of Experts with Adaptive Routing
Mohammed Muqeeth, Haokun Liu, Colin Raffel
Sparsely activated neural networks with conditional computation learn to route their inputs through different "expert" subnetworks, providing a form of modularity that densely acti…
TIES-Merging: Resolving Interference When Merging Models
Prateek Yadav, Derek Tam, Leshem Choshen +2
Transfer learning - i.e., further fine-tuning a pre-trained model on a downstream task - can confer significant advantages, including improved downstream performance, faster conver…
Scaling Data-Constrained Language Models
Niklas Muennighoff, Alexander M. Rush, Boaz Barak +6
The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may so…
Knowledge is a Region in Weight Space for Fine-tuned Language Models
Almog Gueta, Elad Venezian, Colin Raffel +3
Research on neural networks has focused on understanding a single model trained on a single dataset. However, relatively little is known about the relationships between different m…
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data
Alon Albalak, Colin Raffel, William Yang Wang
Few-shot learning is valuable in many real-world applications, but learning a generalizable model without overfitting to the few labeled datapoints is challenging. In this work, we…