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Remember when your elementary school teacher said, "A picture is worth a thousand words"? Well, according to some clever researchers at Google, a picture might actually be worth 16x16 words.

Traditionally, it’s been assumed that LLMs require correctly labeled demonstrations to perform new tasks. But what if the accuracy of these labels isn’t as crucial as we thought? This research raises fascinating questions about how LLMs interpret and use the data they're given.

The researchers behind MM1 did exactly that by diving deep into the world of multimodal large language models (MLLMs). In their paper, they pop the hood and explore the inner workings of these models, revealing some unexpected insights along the way.

Ever wondered if AI models could suddenly develop superpowers as they grow bigger?

The paper “Breadth-First Pipeline Parallelism for Large Language Model Training” introduces a cutting-edge approach aimed at improving the efficiency of training large language models. It tackles some of the key inefficiencies in current training methods, such as the notorious "pipeline bubble" and underutilized GPUs, to offer a more streamlined process.

We discuss what it would be like if we could solve all computer vision tasks with a single, unified model? That’s exactly what the researchers behind the "All in Tokens" approach set out to achieve.

One of the biggest challenges I faced was deciding between fine-tuning and in-context learning—two approaches that have gained a lot of attention recently

In this post, I’ll share what I’ve learned about LoRA, how it works, and why it’s such a game-changer for fine-tuning large AI models

I’m excited to share my insights on this topic and walk you through the process of fine-tuning an LLM to enhance its knowledge and performance in a specific domain

I’ll break down the differences between RAG and fine-tuning, how each works for content summarization, and when to use one over the other

I’ll break down the differences between pre-retrieval and post-generation in RAG, share my own experience with these approaches, and guide you through when to use each.