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  • Mixture-of-Agents Enhances Large Language Model Capabilities
    Motivation In recent years we witness remarkable advancements in AI and specifically in natural language understanding, which are driven by large language models. Today, there are various different LLMs out there such as GPT-4, Llama 3, Qwen, Mixtral and many more. In this post we review a recent paper, titled: “Mixture-of-Agents Enhances Large Language Model Capabilities”, which presents a new method, called Mixture-of-Agents, where LLMs can collaborate together as a team, and harness the collective expertise of different LLMs. So, instead of using a single LLM to get a response, we can get a response that is powered by multiple …

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  • Arithmetic Transformers with Abacus Positional Embeddings
    Introduction In the recent years, we witness remarkable success driven by large language models (LLMs). While LLMs perform well in various domains, such as natural language problems and code generation, there is still a lot of room for improvement with complex multi-step and algorithmic reasoning. To do research about algorithmic reasoning capabilities without pouring significant amount of money, a common approach is to focus on simple arithmetic problems, like addition, since addition of large numbers is a multi-step calculation. In this post we cover a fascinating recent research paper titled: Transformers Can Do Arithmetic with the Right Embeddings, which presents …

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  • CLLMs: Consistency Large Language Models
    In this post we dive into Consistency Large Language Models, or CLLMs in short, which were introduced in a recent research paper that goes by the same name. Before diving in, if you prefer a video format then check out the following video: Motivation Top LLMs such as GPT-4, LLaMA3 and more, are pushing the limits of AI to remarkable advancements. When we feed a LLM with a prompt, it only generates a single token at a time. In order to generate the second token in the response, another pass of the LLM is needed, now both with the prompt …

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  • ReFT: Representation Finetuning for Language Models
    In this post we dive into a recent research paper which presents a promising novel direction for fine-tuning LLMs, achieving remarkable results when considering both parameters count and performance. Before diving in, if you prefer a video format then check out the following video: Motivation – Finetuning a Pre-trained Transformer is Expensive A common method to solve a problem in AI these days is to leverage an existing large pre-trained transformer model, which was already trained on huge amount of data, and in order to make it work better for the specific task that we want to solve, we usually …

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Top Posts

  • Code Llama repository-level reasoning

    Code Llama Paper Explained

    Code Llama is a new family of open-source large language models for code by Meta AI that includes three type of models. Each type was released with 7B, 13B and 34B params. In this post we’ll explain the research paper behind them, titled “Code Llama: Open Foundation Models for Code”, to understand how these models…

  • DINOv2 as foundational model

    DINOv2 from Meta AI – Finally a Foundational Model in Computer Vision

    DINOv2 is a computer vision model from Meta AI that claims to finally provide a foundational model in computer vision, closing some of the gap from natural language processing where it is already common for a while now. In this post, we’ll explain what does it mean to be a foundational model in computer vision…

  • I-JEPA example

    I-JEPA – A Human-Like Computer Vision Model

    I-JEPA, Image-based Joint-Embedding Predictive Architecture, is an open-source computer vision model from Meta AI, and the first AI model based on Yann LeCun’s vision for a more human-like AI, which he presented last year in a 62 pages paper titled “A Path Towards Autonomous Machine Intelligence”.In this post we’ll dive into the research paper that…

  • YOLO-NAS

    What is YOLO-NAS and How it Was Created

    In this post we dive into YOLO-NAS, an improved version in the YOLO models family for object detection which was precented earlier this year by Deci. YOLO models have been around for a while now, presented in 2015 with the paper You Only Look Once, which is what the shortcut YOLO stands for, and over…

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