SR-LLM:一个增量符号回归框架,由基于LLM的检索增强生成驱动
Zelin Guo1, Siqi Wang1, Yonglin Tian2
1Department of Automation, Tsinghua University, Beijing 100084, China.
概括
使用大型语言模型 (LLM) 和检索增强生成的符号回归 (SR) 可实现增量学习. 这种SR-LLM框架有效地利用先前的知识,从数据中发现复杂的,可解释的分析模型.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 符号回归 (SR) 对于从数据中发现分析模型至关重要.
- 现有的SR算法在巨大的搜索空间中扎,限制了复杂表达式的发现.
- 深度学习的进步重新引起了对分析建模的SR的兴趣.
研究的目的:
- 引入SR-LLM,一个新的SR框架,利用大语言模型 (LLM) 和提取增强生成进行增量学习.
- 通过整合先前的知识来增强复杂,可解释的分析表达式的发现.
- 将框架应用于具有挑战性的领域,如人类汽车追踪行为分析.
主要方法:
- SR-LLM将提取增强生成与LLM集成,用于增量学习.
- 该框架将先前的信息组成为符号组,使用LLMs.
- 深度强化学习将这些组组合起来,形成复杂的分析表达式.
主要成果:
- 在标准的SR基准指标上,SR-LLM表现出卓越的性能.
- 该框架成功地从经验数据中重新发现已知的汽车跟踪模型.
- 发现了人类汽车追踪行为的新分析模型,显示了有效性和可解释性.
结论:
- SR-LLM有效地利用先前的知识和过去的探索进行符号回归.
- 该框架有助于发现复杂的,人类可以理解的分析模型.
- SR-LLM提供了一个强大的方法,用于科学发现在各种领域,包括行为分析.
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