Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems
This work addresses the integration challenge in neurosymbolic AI by offering a novel reinterpretation framework, though it appears incremental in applying existing models to symbolic reasoning tasks.
The paper proposes 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 model's internal representation space. Preliminary evaluations across axiomatic deductive reasoning procedures show improvements in learning efficiency and reasoning reliability.
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.