Enhanced Large Language Models as Reasoning Engines

The recent exponential advances in natural language processing capabilities from large language models (LLMs) have stirred tremendous excitement about their potential to achieve human-level intelligence. Their ability to produce remarkably coherent text and engage in dialogue after exposure to vast datasets seems to point towards flexible, general purpose reasoning skills.

However, a growing chorus of voices urges caution against unchecked optimism by highlighting fundamental blindspots that limit neural approaches. LLMs still frequently make basic logical and mathematical mistakes that reveal a lack of systematicity behind their responses. Their knowledge remains intrinsically statistical without deeper semantic structures.

More complex reasoning tasks further expose these limitations. LLMs struggle with causal, counterfactual, and compositional reasoning challenges that require going beyond surface pattern recognition. Unlike humans who learn abstract schemas to flexibly recombine modular concepts, neural networks memorize correlations between co-occurring terms. This results in brittle generalization outside narrow training distributions.

The chasm underscores how human cognition employs structured symbolic representations to enable systematic composability and causal models for conceptualizing dynamics. We reason by manipulating modular symbolic concepts based on valid inference rules, chaining logical dependencies, leveraging mental simulations, and postulating…