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Found 6 papers, 5 papers with code
Retentive Network: A Successor to Transformer for Large Language Models
In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance.
Debiased inference for dynamic nonlinear models with two-way fixed effects
In the maximum likelihood context, this induces an asymptotic bias of the likelihood function.
Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers
We comprehensively compare the behaviors of in-context learning and explicit finetuning on real tasks to provide empirical evidence that supports our understanding.
Structured Prompting: Scaling In-Context Learning to 1,000 Examples
Large language models have exhibited intriguing in-context learning capability, achieving promising zero- and few-shot performance without updating the parameters.
Prototypical Calibration for Few-shot Learning of Language Models
In-context learning of GPT-like models has been recognized as fragile across different hand-crafted templates, and demonstration permutations.
