将大模型视为基于内部表示的符号系统，实现高效学习与可靠推理。
Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems
- 用大模型内部表征作为符号层，自然语言为符号载体。
- 在不同复杂度的公理推理中提升学习效率与推理可靠性。
- 适合对可解释性、推理能力有要求的研究者和应用者。
神经符号人工智能系统结合神经网络与经典符号AI机制，发挥大规模泛化学习与稳健可验证推理的互补优势。现有多种神经符号AI分类展示了两类组件的不同整合方式。本文提出将指令微调的大语言模型重新诠释为基于模型的符号人工智能系统，其中自然语言作为符号层，通过模型内部表示空间实现接地。在此框架下，我们研究并开发了保持传统学习与推理范式结构相似性的新方法。在不同复杂度的公理演绎推理任务上的初步评估，揭示了该方法在提升学习效率与推理可靠性方面的有效性。
原文摘要 · Abstract (English)
Neurosymbolic artificial intelligence (AI) systems combine neural network and classical symbolic AI mechanisms to exploit the complementary strengths of large scale, generalizable learning and robust, verifiable reasoning. Numerous classifications of neurosymbolic AI illustrate how these two components can be integrated in distinctly different ways. In this work, we propose reinterpreting instruction tuned large language models as model grounded symbolic AI systems where natural language serves as the symbolic layer and grounding is achieved through the models internal representation space. Within this framework, we investigate and develop novel learning and reasoning approaches that preserve structural similarities to traditional learning and reasoning paradigms. Preliminary evaluations across axiomatic deductive reasoning procedures of varying complexity provide insights into the effectiveness of our approach in improving learning efficiency and reasoning reliability.
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