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Language models (LMs) proficiency in handling deterministic symbolicreasoning and rule-based tasks remains limited due to their dependency implicitlearning on textual data. To enable fully rule comprehension ability, weexplore how to incorporate compiled neural networks (CoNNs) which weight isspecially designed into the architecture of LMs, to achieve high accuracy androbust performance. CoNNs are transformer-based neural networks that executerules through artificially generated attention weights. Our method, which call"Neural Comprehension", by incorporating CoNN modules into the LM, theframework effectively tackles rule-intensive challenges. Our experiments onsymbolic reasoning tasks and real-world arithmetic reasoning tasks demonstratethe superior performance of our method compared to existing techniques.Furthermore, our LM achieves flawless execution on symbolic operations tasks,highlighting the potential of our method in enabling LMs to possess truesymbolic comprehension capabilities. Our code is publicly available at:https://github.com/WENGSYX/Neural-Comprehension.

Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks