New Theory Suggests Chatbots Can Understand Text, on Quanta Magazine

Quanta Magazine is a nonprofit science publication that covers deep, sourced explainers on math, physics, biology and computer science, and this January 2024 piece by journalist Anil Ananthaswamy takes on one of AI's more contested questions: do large language models understand anything, or are they just "stochastic parrots" recombining text they've already seen?

The article walks through a theory from Princeton's Sanjeev Arora and Google DeepMind's Anirudh Goyal, who model an LLM's skills and the texts that require them as a bipartite graph, then use random graph theory and the field's established neural scaling laws to argue that bigger models don't just get better at individual skills, they start combining skills in ways unlikely to have appeared together in their training data. Quanta reports on the theory's reception too, including AI researcher Geoffrey Hinton and Microsoft's Sebastien Bubeck weighing in, and describes the skill-mix test Arora's team built to probe the idea directly, including a sample GPT-4 output judged against four combined skills at once.

Read this as Quanta's account of a theoretical argument and its early tests, not a settled resolution to the parroting debate; the article itself frames Arora and Goyal's claims as a new theory rather than proof. It suits readers curious about how researchers are trying to formalize what "understanding" might mean for a chatbot.