Have we stopped to think about what LLMs actually model?
In May, Sam Altman, CEO of $80-billion-or-so OpenAI, seemed unconcerned about how much it would cost to achieve the company's stated goal. "Whether we burn $500 million a year or $5 billion – or $50 billion a year – I don't care," he told students at Stanford University. "As long as we can figure out a way to pay the bills, we're making artificial general intelligence. It's going to be expensive."
Statements like this have become commonplace among tech leaders who are scrambling to maximize their investments in large language models (LLMs). Microsoft has put $10 billion into OpenAI, Google and Meta have their own models, and enterprise vendors are baking LLMs into products on a large scale. However, as industry bellwether Gartner identifies GenAI as nearing the peak of the hype cycle, it's time to examine what LLMs actually model – and what they do not.
"Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency" is a recent peer-reviewed paper that aims to take a look at how LLMs work, and examine how they compare with a scientific understanding of human language.
