Exploring the Connection Between Text Diffusion Models and BERT's Masked Language Modeling
By
nathan-barry
Crackling crust, pillowy middle. The kind of bagel that earns a second cup of coffee.
Summary
This article explores the connection between diffusion models for text generation and traditional masked language modeling (MLM) used in BERT models. The author discovers that discrete language diffusion is essentially a generalization of MLM, and conducts a proof-of-concept experiment to see if BERT-like models can be fine-tuned for text generation tasks. The piece discusses Google DeepMind's Gemini Diffusion model and compares it with traditional GPT-style generation approaches.
Key quotes
· 4 pulleddiscrete language diffusion is just a generalization of masked language modeling (MLM), something we've been doing since 2018
can we finetune a BERT-like model to do text generation?
Unlike traditional GPT-style models that generate one word at a time, Gemini Diffusion creates whole blocks of text by refining random noise step-by-step
I decided to try a quick proof of concept out of curiosity
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