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TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings
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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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Cited by 1 Pith paper
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Unifying Continuous and Discrete Text Diffusion with Non-simultaneous Diffusion Processes
NeoDiff uses a Poisson forward process with per-token noise timing and a learned time predictor to improve non-autoregressive text diffusion generation.
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