Summary of Difflm: Controllable Synthetic Data Generation Via Diffusion Language Models, by Ying Zhou et al.
DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models
by Ying Zhou, Xinyao Wang, Yulei Niu, Yaojie Shen, Lexin Tang, Fan Chen, Ben He, Le Sun, Longyin Wen
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces a controllable data synthesis framework called DiffLM, which leverages large language models (LLMs) to generate high-quality synthetic data for structured formatted data such as Tabular, Code, and Tool data. The authors address the limitations of LLMs in understanding target data distributions and prompt engineering by decoupling the learning of target distribution knowledge from the LLM’s generative objectives via a latent feature injection module. Additionally, they incorporate a diffusion model to reserve more information about original data distributions and formats. The proposed framework, DiffLM, is evaluated on seven real-world datasets and demonstrates significant performance gains over real data in certain cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for computers to create high-quality fake data that looks like real data. This can be very useful for things like training artificial intelligence models or testing how well they work. The researchers developed a new way of using large language models, which are already good at generating text, to create synthetic data that has the same structure as real data. They tested their method on different types of data and found that it worked better than just using real data in some cases. |
Keywords
» Artificial intelligence » Diffusion model » Prompt » Synthetic data
