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Found 4 papers, 3 papers with code
Linearizing Large Language Models
Linear transformers have emerged as a subquadratic-time alternative to softmax attention and have garnered significant interest due to their fixed-size recurrent state that lowers inference cost.
Language models scale reliably with over-training and on downstream tasks
We fit scaling laws that extrapolate in both the number of model parameters and the ratio of training tokens to parameters.
A Critical Evaluation of AI Feedback for Aligning Large Language Models
RLAIF first performs supervised fine-tuning (SFT) using demonstrations from a teacher model and then further fine-tunes the model with reinforcement learning (RL), using feedback from a critic model.
Asking More Informative Questions for Grounded Retrieval
When a model is trying to gather information in an interactive setting, it benefits from asking informative questions.
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