This new, dead simple prompt technique boosts accuracy on LLMs by up to 76% on non-reasoning tasks

Most modern LLMs are trained as "causal" language models. This means they process text strictly from left to right. When the ...
IEEE

Wi-Fi-Based Human Fall and Activity Recognition Using Transformer-Based Encoder–Decoder and Graph Neural Networks

Abstract: Human pose estimation and action recognition have received attention due to their critical roles in healthcare monitoring, rehabilitation, and assistive technologies. In this study, we ...
IEEE

A Novel Length Controllable Encoder-Decoder Transformer Model for Abstractive Summarization of Scientific Documents

Abstract: A growing number of scientific publications are available today. As this data grows, it becomes increasingly important to use semantic density to convey the most essential information as ...
marktechpost

Meet LLMRouter: An Intelligent Routing System designed to Optimize LLM Inference by Dynamically Selecting the most Suitable Model for Each Query

LLMRouter is an open source routing library from the U Lab at the University of Illinois Urbana Champaign that treats model selection as a first class system problem. It sits between applications and ...
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Transformer encoder architecture explained simply

We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT process text, this is your ultimate guide. We look at the entire design of ...