Nvidia’s new technique cuts LLM reasoning costs by 8x without losing accuracy

Nvidia researchers developed dynamic memory sparsification (DMS), a technique that compresses the KV cache in large language models by up to 8x while maintaining reasoning accuracy — and it can be ...
The Lancet

The end of animal testing?

Both the UK and US Governments have pledged to end research using animals, but is such a goal realistic? And how might it change medical research? Talha Burki reports.
IEEE

Software Testing with Large Language Models: An Interview Study with Practitioners

Abstract: Background: The use of large language models in software testing is growing fast as they support numerous tasks, from test case generation to automation, and documentation. However, their ...
IEEE

A Framework for Identifying Emotions in Text through Machine Learning and Natural Language Processing Methods

Abstract: This research introduces an innovative approach that leverages machine learning and natural language processing (NLP) techniques to detect emotions in text. The proposed system, equipped ...