Search Results for author:
Found 7 papers, 4 papers with code
OLMo: Accelerating the Science of Language Models
Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs.
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Language models have become a critical technology to tackling a wide range of natural language processing tasks, yet many details about how the best-performing language models were developed are not reported.
Paloma: A Benchmark for Evaluating Language Model Fit
We invite submissions to our benchmark and organize results by comparability based on compliance with guidelines such as removal of benchmark contamination from pretraining.
How To Train Your (Compressed) Large Language Model
With the increase in the size of large language models (LLMs), we need compression methods that can reduce the model size while preserving the generality and zero-shot promptability of the model.
AAVAE: Augmentation-Augmented Variational Autoencoders
We empirically evaluate the proposed AAVAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated.
AASAE: Augmentation-Augmented Stochastic Autoencoders
We empirically evaluate the proposed AASAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated.
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations.
