Papers by Bocheng Li
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. |
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. |
A Structure-Aware Generative Adversarial Network for Bilingual Lexicon Induction (2023.findings-emnlp)
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| Challenge: | Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. |
| Approach: | They propose a model that explicitly captures multiple topological structure information to achieve accurate bilingual lexicon induction. |
| Outcome: | The proposed model captures multiple topological structure information to achieve accurate BLI on a public dataset. |