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Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text.
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Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Yang Song and Stefano Ermon. 2020 · 2020
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DialogSum: A real-life scenario dialogue summarization dataset
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SimCSE: Simple contrastive learning of sentence embeddings
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Can diffusion model achieve better performance in text generation ? bridging the gap between training and inference !
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SEAHORSE: A multilingual, multifaceted dataset for summarization evaluation
Elizabeth Clark, Shruti Rijhwani, Sebastian Gehrmann, Joshua Maynez, Roee Aharoni, Vitaly Nikolaev, Thibault Sellam, Aditya Siddhant, Dipanjan Das, and Ankur Parikh. 2023 · 2023
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Diffuseq: Sequence to sequence text generation with diffusion models
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DiffuSeq-v2: Bridging discrete and continuous text spaces for accelerated Seq2Seq diffusion models
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A survey of large language models
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
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Diffusion models in text generation: a survey
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Pixel-space post-training of latent diffusion models
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Diffusion-NAT: Self-prompting discrete diffusion for non-autoregressive text generation
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