Summary of Simple and Effective Masked Diffusion Language Models, by Subham Sekhar Sahoo et al.
Simple and Effective Masked Diffusion Language Models
by Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, Volodymyr Kuleshov
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an innovative approach to language modeling using masked discrete diffusion, which outperforms previous methods. The authors develop a simplified objective that combines classical masked language modeling losses and achieves state-of-the-art results among diffusion models. They also provide the code, along with a blog post and video tutorial, allowing researchers to replicate their findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how simple masked discrete diffusion can be used for language modeling. It’s more effective than previously thought! The authors share a recipe for training masked diffusion models that works well, and they even come up with a new way of combining objectives that helps even more. They achieve the best results among diffusion models and get close to autoregressive methods. You can check out their code, blog post, and video tutorial on their project page. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion
