<ul><li>Neuro-symbolic learning aims to combine neural networks with symbolic programs for complex reasoning tasks for better interpretability, reliability, and efficiency.</li><li>Traditional neuro-symbolic learning methods face challenges limiting them to simplistic problems, while purely-neural foundation models achieve state-of-the-art performance through prompting but lack interpretability and reliability.</li><li>Combining foundation models with symbolic programs, known as neuro-symbolic prompting, offers a solution for complex reasoning tasks and raises questions about specialized model training in the era of foundation models.</li><li>Foundation models pave the way for generalizable neuro-symbolic solutions, addressing compute, data, and program-related pitfalls of traditional neuro-symbolic learning and offering a path towards achieving original goals without the downsides of training from scratch.</li></ul>

The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models

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