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-language-diffusion-models/
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

#AI #MachineLearning #DiffusionModels #LLMs #ArtificialIntelligence #LLaDA #DeepLearning #AutoregressiveModels #Research #llada #naturallanguageprocessing #languagemodels #largelanguagemodels

AI Papers Academy

A new research paper introduces LaDDer (Large Language Diffusion with Masking), a diffusion-based alternative to autoregressive language models. Instead of generating tokens sequentially left-to-right, LaDDer uses a masking-based diffusion process: during training, tokens are randomly masked and a Transformer-based mask predictor learns to restore them. At inference, the model iteratively unmasks a fully masked response using reverse diffusion, with remasking strategies based on prediction confidence or semi-autoregressive block processing. Trained on 2.3 trillion tokens for pre-training and 4.5 million samples for supervised fine-tuning, the 8B parameter LaDDer model is competitive with LLaMA 3 on several benchmarks, shows strong scalability on math tasks (GSM8K), and notably outperforms GPT-4o and Qwen 2.5 on reversal poem completion — a task where autoregressive models inherently struggle due to left-to-right constraints.

Large Language Diffusion Models - The Era Of Diffusion LLMs?