Summary of Energy-based Diffusion Language Models For Text Generation, by Minkai Xu et al.
Energy-Based Diffusion Language Models for Text Generation
by Minkai Xu, Tomas Geffner, Karsten Kreis, Weili Nie, Yilun Xu, Jure Leskovec, Stefano Ermon, Arash Vahdat
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 A recently emerging alternative to autoregressive language models is discrete diffusion models, which can generate text in parallel. However, these models have been shown to underperform their autoregressive counterparts, especially when reducing the number of sampling steps. In this work, researchers propose an Energy-based Diffusion Language Model (EDLM) that operates at the full sequence level for each diffusion step to improve the underlying approximation used by diffusion models. The EDLM is based on energy-based models and can be trained using a pre-trained autoregressive model or via noise contrastive estimation. An efficient generation algorithm is also proposed, which uses parallel important sampling. Experiments show that EDLM outperforms state-of-the-art diffusion models by a significant margin, approaches the perplexity of autoregressive models, and offers a 1.3x speedup without sacrificing generation performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special way to generate text, like writing stories or answering questions. This way is called “autoregressive” because it uses what it already knows to predict what comes next. There’s another way to do this called “discrete diffusion models,” which can also generate text in parallel. But these models haven’t been doing as well as the autoregressive ones, especially when they don’t have as much information to work with. To fix this, some researchers came up with a new idea called Energy-based Diffusion Language Model (EDLM). It’s like a special tool that helps diffusion models make better predictions. They also came up with an efficient way to use this tool, which is faster than other methods without sacrificing quality. |
Keywords
» Artificial intelligence » Autoregressive » Diffusion » Language model » Perplexity
