基于DSPy的神经符号管道,以增强LLM中的空间推理
1Department of Computational Linguistics, University of Tübingen, Tübingen, Germany; Institute of Natural Language Processing, University of Stuttgart, Stuttgart, Germany.
概括
我们开发了一个新的神经符号框架,将大型语言模型 (LLM) 与答案集编程 (ASP) 集成,以改进空间推理. 我们的方法显著提高了复杂任务的准确性,优于现有的方法.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 认知科学 认知科学
背景情况:
- 空间推理是大型语言模型 (LLM) 的关键能力,但它仍然是一个重大的持续挑战.
- 目前的神经符号方法提供有限的解决方案,由于不灵活的设计和低于最佳的有效性.
研究的目的:
- 引入一种新的神经符号框架,增强LLM空间推理能力.
- 通过代反来实现LLM产生的逻辑程序的精确和可靠的改进.
主要方法:
- 通过代反循环将LLMs与答案集编程 (ASP) 集成在一起.
- 语义解析和逻辑推理组件的模块化分离.
- 为高效计算开发特定领域的符号表示.
主要成果:
- 在基准数据集上实现了82-93%和71-80%的准确性,显著超过了直接提示和思维链方法.
- 拟议的轻量级"事实+规则"替代方案提高了9-27%的基线性能,降低了计算开销.
- 与两阶段"事实+规则"方法相比,表现优越,高达43%和25%.
结论:
- 该框架为LLMs提供了精确,可靠和可解释的空间推理.
- 它在多样化和复杂的任务中显示出强大的概括性,为人类类推理推进AI架构.
- 该系统通过实现多组件推理,为人工通用智能的发展做出了贡献.
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