通过基于规则的推理进行序列对序列学习的生成模糊系统
IEEE transactions on neural networks and learning systems
|October 28, 2025
概括
生成模糊系统 (GenFS) 通过结合数据驱动和知识驱动的方法来增强大型语言模型 (LLM). 拟议的FuzzyS2S模型改进了序列生成任务,如翻译和代码生成.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 模糊系统 (Fuzzy Systems) 是一个模糊系统.
背景情况:
- 生成模型 (GMs),包括大型语言模型 (LLMs),在创建新数据方面表现出色,但往往缺乏可解释性和更广泛的知识获取.
- 它们的复杂,数据驱动的性质阻碍了对输出的理解和控制,限制了稳定性和概括性.
研究的目的:
- 通过整合数据驱动和知识驱动机制来增强生成任务.
- 引入一种新的生成模糊系统 (GenFS) 框架,将GM深度学习与模糊系统可解释性相结合.
- 为序列生成开发一个端到端的基于GenFS的模型.
主要方法:
- 利用模糊系统,一种经典的建模方法,来补充GM.
- 提出了GenFS框架,将深度学习与模糊逻辑的基于术语的可解释性和双驱动机制相结合.
- 开发用于序列生成任务的FuzzyS2S模型.
主要成果:
- 在机器翻译,代码生成和摘要生成的12个数据集上进行了测试研究.
- 与变压器模型相比,FuzzyS2S表现出更高的准确性和流性.
- 在特定应用场景中超越了最新的T5和CodeT5模型.
结论:
- 通过结合知识驱动的洞察力,GenFS框架有效地增强了生成模型.
- FuzzyS2S为更强大和可解释的序列生成提供了一个有前途的方法.
- 这种混合方法显示了在复杂的生成任务中推进AI能力的潜力.
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