Large Language Diffusion Models - The Era Of Diffusion LLMs?
Large language models (LLMs) have revolutionized AI, but they primarily rely on autoregressive (AR) generation, where tokens are predicted one by one in sequence. While effective, this approach has limitations, including high computational costs and challenges with tasks requiring reversal reasoning.
In this video, we dive into the Large Language Diffusion Models (LLaDA) paper, which introduces a novel approach to language modeling using diffusion models. Traditionally used in image generation, diffusion models offer a bidirectional alternative to AR methods. We'll explore:
🔹 A quick recap of diffusion models in computer vision
🔹 How LLaDA adapts diffusion models to LLMs
🔹 The training and inference process of Large Language Diffusion Models
🔹 Performance comparisons and scaling trends
🔹 How LLaDA breaks the "reversal curse"
Written Review - https://aipapersacademy.com/large-lan...
Paper - https://arxiv.org/abs/2502.09992
GitHub Page - https://ml-gsai.github.io/LLaDA-demo/
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The video was edited using VideoScribe - https://tidd.ly/44TZEiX
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Chapters:
0:00 Introduction
1:44 Diffusion Models Recap
2:33 Large Language Diffusion Models Intuition
3:25 LLaDA Training Process
5:25 LLaDA Inference
7:11 Results
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