Papers by Longyin Wen
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models (2025.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for high-quality data synthesis. |
| Approach: | They propose a controllable data synthesis framework based on variational autoencoder which leverages diffusion models to reserve more information of original distribution and format structure in the learned latent distribution. |
| Outcome: | The proposed framework generates high-quality data with performance exceeding that of real data by 2%–7% on seven real-world datasets. |