The Emerging Capacity for Thought in Large Language Models
For decades, artificial intelligence research has grappled with the challenge of representing knowledge in a way that allows machines to reason and solve problems. Customary approaches, like higher-order predicate calculi, excel at formalizing precise relationships but struggle with the nuances of human understanding – the abstract, the imprecise, the context-dependent. Natural language, conversely, possesses a remarkable completeness.It allows us to articulate any concept, at any level of detail, even concepts about language itself, making it a compelling foundation for knowledge depiction.
The hurdle,of course,is the inherent complexity of natural language.historically, parsing and understanding it required painstaking manual programming. However,a paradigm shift is underway: we are now leveraging the power of data and training to enable machines to learn directly from language.
This approach centers around the “next-token prediction” task.A model learns to predict the most probable subsequent word given a sequence of preceding words. Crucially, achieving accuracy in this seemingly simple task necessitates internal representation of world knowledge. Consider the prompt: “The highest mountain peak in the world is Mount…” - correctly predicting “Everest” demands pre-existing knowledge. More complex reasoning, like solving puzzles, requires the model to generate a “Chain of Thought” (CoT) – a sequence of intermediate tokens that demonstrate the logical steps taken to arrive at a solution. This implies a working memory capable of holding and manipulating several tokens, maintaining a coherent line of reasoning.
Interestingly, this process mirrors human cognition.We, too, constantly predict the next word – in speech, in internal monologue. A truly perfect auto-complete system, capable of flawlessly predicting and generating correct answers, would require omniscience, an unattainable ideal. However, a model capable of learning through data, adjusting its internal parameters, and benefiting from reinforcement can demonstrably learn to think.
But does it actually produce the effects of thinking?
The ultimate arbiter is performance on tasks requiring genuine reasoning. If a system can successfully answer novel questions demanding logical thought, it suggests the emergence of reasoning capabilities – or at least, a convincing simulation thereof. While proprietary Large Language Models (LLMs) show extraordinary results on reasoning benchmarks,concerns about potential data contamination (fine-tuning on the test sets themselves) necessitate a focus on open-source models for a fair and transparent evaluation.
Recent evaluations using established benchmarks reveal that open-source LLMs are capable of solving a significant proportion of logic-based problems. While they often fall short of human performance, it’s vital to remember that human baselines frequently represent individuals specifically trained on those benchmarks. In some instances, LLMs even surpass the performance of the average untrained human.
Conclusion:
Considering the benchmark results, the striking parallels between cot reasoning and biological reasoning, and the theoretical principle that any sufficiently powerful computational system, equipped with adequate data and processing capacity, can perform any computable task, LLMs appear to meet these criteria to a substantial degree.
Therefore, it is reasonable to conclude that LLMs almost certainly possess the ability to think – or, at the very least, to exhibit behaviors indistinguishable from thought.
(Author Note: debasish Ray Chawdhuri is a senior principal engineer at Talentica Software and a Ph.D. candidate in Cryptography at IIT Bombay.)
