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arXiv · 2402.19097

TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings

Alexander Shabalin·Viacheslav Meshchaninov·Egor Chimbulatov·Vladislav Lapikov·Roman Kim·Grigory Bartosh·Dmitry Molchanov·Sergey Markov·Dmitry Vetrov
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Abstract

This paper presents the Text Encoding Diffusion Model (TEncDM), a novel approach to diffusion modeling that operates in the space of pre-trained language model encodings. In contrast to traditionally used embeddings, encodings integrate contextual information. In our approach, we also employ a transformer-based decoder, specifically designed to incorporate context in the token prediction process. We conduct a comprehensive examination of the influence of the encoder, decoder, noise scheduler, and self-conditioning on zero-shot generation. Furthermore, we compare TEncDM with previous approaches on three conditional text generation tasks: QQP, XSum, and Wiki-Auto. The results show that TEncDM exhibits superior performance compared to existing non-autoregressive diffusion models. Our code is available at https://github.com/M0RJIQUE/tencdm.

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Alexander Shabalin, Viacheslav Meshchaninov, Egor Chimbulatov, Vladislav Lapikov, Roman Kim, Grigory Bartosh, Dmitry Molchanov, Sergey Markov, Dmitry Vetrov. 2025-02-24. TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings. https://arxiv.org/abs/2402.19097

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