What should we believe about the reasoning abilities of today’s large language models? As the headlines above illustrate, there’s a debate raging over whether these enormous pre-trained neural networks have achieved humanlike reasoning abilities, or whether their skills are in fact “a mirage.” Reasoning is a central aspect of human intelligence, and robust domain-independent reasoning abilities have long been a key goal for AI systems. While large language models (LLMs) are not explicitly trained to reason, they have exhibited “emergent” behaviors that sometimes look like reasoning. But are these behaviors actually driven by true abstract reasoning abilities, or by some other less robust and generalizable mechanism—for example, by memorizing their training data and later matching patterns in a given problem to those found in training data? Why does this matter? If robust general-purpose reasoning abilities have emerged in LLMs, this bolsters the claim that such systems are an important step on the way to trustworthy general intelligence. On the other hand, if LLMs rely primarily on memorization and pattern-matching rather than true reasoning, then they will not be generalizable—we can’t trust them to perform well on “out of distribution” tasks, those that are not sufficiently similar to tasks they’ve seen in the training data.

Full opinion : How LLMs are taught to reason, the downsides of “chain of thought”, how models might solve problems without reasoning, and evaluating LLMs for general reasoning.