LLMs
Researchers teach LLMs to solve complex planning challenges
Imagine a coffee company trying to optimize its supply chain. The company sources beans from three suppliers, roasts them at two facilities into either dark or light coffee, and then ships the roasted coffee to three retail locations. The suppliers have different fixed capacity, and roasting costs and shipping costs vary from place to place….
NVIDIA AI Researchers Introduce FFN Fusion: A Novel Optimization Technique that Demonstrates How Sequential Computation in Large Language Models LLMs can be Effectively Parallelized
Large language models (LLMs) have become vital across domains, enabling high-performance applications such as natural language generation, scientific research, and conversational agents. Underneath these advancements lies the transformer architecture, where alternating layers of attention mechanisms and feed-forward networks (FFNs) sequentially process tokenized input. However, with an increase in size and complexity, the computational burden required…
How Well Can LLMs Actually Reason Through Messy Problems?
The introduction and evolution of generative AI have been so sudden and intense that it’s actually quite difficult to fully appreciate just how much this technology has changed our lives. Zoom out to just three years ago. Yes, AI was becoming more pervasive, at least in theory. More people knew some of the things it…
How Yelp reviewed competing LLMs for correctness, relevance and tone to develop its user-friendly AI assistant
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The review app Yelp has provided helpful information to diners and other consumers for decades. It had experimented with machine learning since its early years. During the recent explosion in AI technology, it was still encountering…
Reinforcement Learning Meets Chain-of-Thought: Transforming LLMs into Autonomous Reasoning Agents
Large Language Models (LLMs) have significantly advanced natural language processing (NLP), excelling at text generation, translation, and summarization tasks. However, their ability to engage in logical reasoning remains a challenge. Traditional LLMs, designed to predict the next word, rely on statistical pattern recognition rather than structured reasoning. This limits their ability to solve complex problems…
A closer look at LLM’s hyper growth and AI parameter explosion
The rapid evolution of artificial intelligence (AI) has been marked by the rise of large language models (LLMs) with ever-growing numbers of parameters. From early iterations with millions of parameters to today’s tech giants boasting hundreds of billions or even trillions, the sheer scale of these models is staggering. Table 1 outlines the number of…
What are Large Language Model (LLMs)?
Understanding and processing human language has always been a difficult challenge in artificial intelligence. Early AI systems often struggled to handle tasks like translating languages, generating meaningful text, or answering questions accurately. These systems relied on rigid rules or basic statistical methods that couldn’t capture the nuances of context, grammar, or cultural meaning. As a…
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