Assessing Gender Bias Transfer Between Pretrained and Rapidly Adapted Language Models
*Equal taxpayers Large language models (LLMs) are increasingly tailored to accomplish specific tasks for implementation in real-world decision systems. Several ...
Tag: PreTrained
Tucano: a series of natively pre-trained decoder-transformers in Portuguese
Natural language processing (NLP) has advanced significantly with deep learning, driven by innovations such as word embedding and transformative architectures. ...
Stanford University Researchers Propose ExPLoRA: A Highly Effective AI Technique to Improve Transfer Learning of Pretrained Vision Transformers (ViT) on Domain Changes
Efficient parameter fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA), allow large pre-trained baseline models to adapt to subsequent tasks ...
SaRA: A memory-efficient fine-tuning method for improving pre-trained diffusion models
Recent advances in diffusion models have significantly improved tasks such as image, video, and 3D generation, with pre-trained models such ...
Representations of the pre-trained basic model for discovering breathing patterns in speech
The process of human speech production involves coordinated respiratory action to obtain acoustic speech signals. Typically, speech is produced by ...
Whispering experts: mitigating toxicity in pre-trained language models by attenuating expert neurons
A major problem with large language models (LLMs) is their unintended ability to generate toxic language. In this work, we ...
Understanding the XLNet Pretrained Model
Introduction XLNet is an autoregressive pretraining method proposed in the article "XLNet: Generalized Autoregressive Pretraining for Language Understanding". XLNet uses ...
Efficient Labeled Sleep Staging Using Pretrained Transformers with Position Prediction
Sleep staging is a clinically important task for diagnosing various sleep disorders, but its implementation at scale remains challenging because ...
This AI article from SambaNova presents a machine learning method to adapt pre-trained LLMs to new languages
The rapid advancement of large language models has ushered in a new era of natural language processing capabilities. However, a ...
Meet BiLLM: a novel post-training binary quantization method designed specifically to compress pre-trained LLMs
Pretrained large language models (LLMs) have remarkable language processing capabilities, but require substantial computational resources. Binarization, which reduces model weights ...
