Summary of Generative Fuzzy System For Sequence Generation, by Hailong Yang et al.
Generative Fuzzy System for Sequence Generation
by Hailong Yang, Zhaohong Deng, Wei Zhang, Zhuangzhuang Zhao, Guanjin Wang, Kup-sze Choi
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 The paper introduces a novel framework called GenFS, which combines generative models with classical fuzzy systems to enhance their robustness and generalization capabilities. The GenFS-based model, FuzzyS2S, is an end-to-end sequence generation model that outperforms the Transformer in terms of accuracy and fluency on 12 datasets across three distinct categories: machine translation, code generation, and summary generation. Specifically, it demonstrates better performance on some datasets compared to state-of-the-art models T5 and CodeT5. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a new way to make computers generate text by combining two old ideas from the 1970s. It’s like taking two recipes and mixing them together to get something new and better! The old ideas are called “generative models” and “fuzzy systems”. Generative models can create text that looks real, but they don’t always make sense. Fuzzy systems can help make sense of things by using rules and logic. When you combine the two, you get a new model that is better at creating text that makes sense and is useful. |
Keywords
* Artificial intelligence * Generalization * T5 * Transformer * Translation
