Recent research shows that more data and larger models can provide more
accurate solutions to natural language problems requiring reasoning. However,
models can easily fail to provide solutions in unobserved complex input
compositions due to not achieving the level of abstraction required for
generalizability. To alleviate this issue, we propose training the language
models with neuro-symbolic techniques that can exploit the logical rules of
reasoning as constraints and provide additional supervision sources to the
model. Training models to adhere to the regulations of reasoning pushes them to
make more effective abstractions needed for generalizability and transfer
learning. We focus on a challenging problem of spatial reasoning over text. Our
results on various benchmarks using multiple language models confirm our
hypothesis of effective domain transfer based on neuro-symbolic training.

通过使用神经符号技术对语言模型进行培训，可以利用推理的逻辑规则作为约束，并为模型提供额外的监督来源，以弥补由于无法达到所需的抽象水平而在未观察到的复杂输入组合中难以提供解决方案的问题。我们聚焦于文本上的空间推理这一具有挑战性的问题，通过在多个语言模型上的多个基准测试中的结果验证了基于神经符号训练的有效领域转移假设。