The rise of large language models (LLMs) like GPT has brought about incredible advances in how AI understands and generates human language. These models have shown impressive capabilities in conversation, writing, and even mimicking complex reasoning. As LLMs scale and ingest vast amounts of data, their ability to “reason” seems to improve dramatically, leading to some confusion about what’s actually happening under the hood.
In this blog post, we’ll explore how LLMs learn, why they seem to get better at reasoning as they scale, and why they still fundamentally differ from human reasoning, which relies on explicit rules and symbolic logic.
LLMs are essentially very powerful pattern recognition systems. During training, they are exposed to enormous datasets made up of text, and they learn to predict the next word or sequence of words based on what they’ve seen. They do this by identifying the statistical relationships between words and concepts—capturing correlations, associations, and patterns in the data.
For example, if you feed an LLM enough examples of sentences like “If A + B, then C,” it will learn that A and B often appear together with C. The more data it has, the better it becomes at making these predictions. This is why scaling LLMs (giving them more data and more parameters) leads to better performance—more data allows the model to capture more patterns, and more parameters enable it to represent these patterns more intricately.
At first, with small datasets and fewer parameters, an LLM’s ability to “reason” is quite limited. It can make simple predictions based on direct statistical associations but struggles with more complex tasks that require deeper understanding. However, as LLMs scale and learn from larger datasets, they start to recognise more complex patterns. These models are able to make predictions that seem much closer to human-like reasoning.
For example, an LLM might predict that “If it rains, the ground will be wet” simply because it has seen many instances of this relationship in the data. As it scales, the model might even handle more abstract reasoning, like analogies or basic cause-and-effect relationships. This leads to what we call emergent behaviour, where the model exhibits capabilities that weren’t explicitly programmed but arise from the patterns it’s seen in the data.
At this stage, the model seems like it’s reasoning, and for many tasks, its predictions are indistinguishable from what a human might deduce. However, there’s a crucial difference between what the LLM is doing and what humans do when they reason.
Despite their remarkable capabilities, LLMs still operate in a probabilistic way. They predict the most likely outcome based on the patterns they’ve learned from their training data. This means that even if they appear to “understand” certain relationships, they’re not actually applying hard-and-fast rules like humans do. Instead, they are generating the next word, sentence, or concept based on statistical likelihood, not on a deep understanding of logic or rules.
• An LLM might “know” that “A + B = C” is a common pattern because it has seen it many times.
• But it can also make mistakes, predicting something illogical or incorrect, because it’s ultimately making probabilistic guesses, not applying a consistent rule.
LLMs don’t deduce things in the way humans do with symbolic reasoning. Humans don’t just rely on patterns—they create and apply explicit rules (like “If A + B, then always C”). This ability to form rules and follow them consistently allows humans to reason logically and avoid errors in situations where patterns might be misleading.
Symbolic reasoning is the type of rule-based logic that humans use. It involves:
• Explicit rules: For example, “All humans are mortal” and “Socrates is a human” lead to the conclusion “Socrates is mortal.” This kind of deduction follows strict rules of logic.
• Consistency: Humans apply these rules consistently across different contexts, regardless of how common or rare a pattern is in the data.
• Understanding exceptions: Humans can also recognise when a rule doesn’t apply, or when an exception exists, and modify their reasoning accordingly.
LLMs, on the other hand, don’t explicitly form or apply these kinds of rules. They approximate reasoning based on the patterns they’ve observed, but without true understanding or the ability to create new rules. This is why, even though LLMs can appear to reason well in many cases, they can still make errors that seem nonsensical from a human perspective.
As LLMs continue to scale, they’ll likely become even better at mimicking human reasoning because they’ll encounter even more patterns and data. However, they’ll still be fundamentally limited by their lack of explicit rules. Unless LLMs are combined with other systems that can encode and apply rules—like symbolic reasoning systems or hybrid models—they won’t achieve the kind of consistent, rule-based reasoning that humans use.
There’s growing interest in creating hybrid AI models that combine the best of both worlds:
• Neural networks (like LLMs) for pattern recognition and dealing with unstructured data.
• Symbolic reasoning systems for applying formal logic, rules, and consistent deductions.
These hybrid models could bridge the gap between the probabilistic reasoning of LLMs and the symbolic, rule-based reasoning that humans excel at. This approach could allow AI systems to handle a wider variety of tasks with greater accuracy, from understanding natural language to performing complex logical deductions in fields like mathematics, law, or scientific reasoning.
LLMs have come a long way in mimicking human-like reasoning, thanks to their ability to scale and learn from vast amounts of data. But they’re still fundamentally pattern-based systems, making probabilistic guesses rather than applying strict, logical rules. While scaling helps them get better at recognising patterns and associations, they don’t yet reason like humans, who use explicit rules to guide their thinking.
Understanding this distinction is important as we continue to develop more advanced AI systems. As hybrid models evolve, combining the strengths of LLMs with symbolic reasoning, we may see the next generation of AI systems capable of both flexible language understanding and robust, rule-based logic.
