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Found 3 papers, 2 papers with code
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models
Currently, most machine learning models are trained by centralized teams and are rarely updated.
Soft Merging of Experts with Adaptive Routing
To address this issue, we introduce Soft Merging of Experts with Adaptive Routing (SMEAR), which avoids discrete routing by using a single "merged" expert constructed via a weighted average of all of the experts' parameters.
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
ICL incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction is made.
Ranked #1 on
Few-Shot Text Classification
on RAFT
