Papers by Zhujin Gao
Unifying Continuous and Discrete Text Diffusion with Non-simultaneous Diffusion Processes (2025.acl-long)
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| Challenge: | Experimental results demonstrate NeoDiff’s superior performance compared to baselines of non-autoregressive continuous and discrete diffusion models, iterative-based methods and autoregressive diffusion-based approaches. |
| Approach: | They propose a discrete and continuous diffusion model that integrates the strengths of discrete, continuous and continuous approaches. |
| Outcome: | The proposed model unifies the theories of discrete and continuous diffusion models, offering a more principled and effective framework for text generation. |
DiffS2UT: A Semantic Preserving Diffusion Model for Textless Direct Speech-to-Speech Translation (2023.emnlp-main)
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| Challenge: | Existing models for speech generation are not efficient due to low information density of speech data. |
| Approach: | They propose a method to integrate discrete diffusion models into speech generation tasks . they propose to apply diffusion forward process while employing diffusion backward process . |
| Outcome: | The proposed model achieves comparable results to the auto-regressive baselines with significantly fewer decoding steps (50 steps). |
Empowering Diffusion Models on the Embedding Space for Text Generation (2024.naacl-long)
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| Challenge: | Recent work adapts diffusion models to textual data by diffusing on the embedding space. |
| Approach: | They propose an embedding diffusion model based on Transformer to solve the problem of embeddable space and denoising model. |
| Outcome: | The proposed model is more efficient than previous methods on seminal text generation tasks and is superior to existing models. |
Few-shot Temporal Pruning Accelerates Diffusion Models for Text Generation (2024.lrec-main)
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| Challenge: | Existing acceleration methods for text generation ignore the importance of the distribution of sampling steps, resulting in slow sampling rates. |
| Approach: | They propose a technique to accelerate diffusion models for text generation without additional training by using a Bayesian optimization approach. |
| Outcome: | The proposed technique achieves 400x acceleration even with minimal sampling steps after down to less than 1 minute of optimization yielding a competitive performance even with minimum sampling steps. |