Large Language Model

Matrix multiplication (MatMul) is a fundamental process in most neural network topologies. It is commonly used in vector-matrix multiplication (VMM) by dense layers in neural networks, and in matrix-matrix multiplication (MMM) by self-attention mechanisms. Significant reliance on MatMul can be attributed to GPU optimization for these tasks. Libraries like cuBLAS and the Compute Unified Device…

Stanford University researchers have developed a new method called Demonstration ITerated Task Optimization (DITTO) designed to align language model outputs directly with users' demonstrated behaviors. This technique was introduced to address the challenges language models (LMs) face - including the need for big data sets for training, generic responses, and mismatches between universal style and…

Large language models (LLMs) have significantly advanced code generation, but they develop code in a linear fashion without access to a feedback loop that allows for corrections based on the previous outputs. This creates challenges in correcting mistakes or suggesting edits. Now, researchers at the University of California, Berkeley, have developed a new approach using…

The field of multimodal learning, which involves training models to understand and generate content in multiple formats such as text and images, is evolving rapidly. Current models have inefficiencies in dealing with text-only and text-image tasks, often excelling in one domain but underperforming in the other. This necessitates distinct systems to retrieve different forms of…

LLM or Language Model-based systems have shown potential to accelerate scientific discovery, especially in the biomedical research field. These systems are able to leverage a large bank of background information to conduct and interpret experiments, particularly useful for identifying drug targets through CRISPR-based genetic modulation. Despite the promise they show, their usage in designing biological…