Beyond Probability: The Limits and Real Potential of Language Models like GPT-4

The limits and potential of language models like GPT-4 have been the topic of a recent discussion on Hacker News. The question raised is whether these models, which can perform complex probability calculations, can truly understand concepts like humans do. While these models can be a game-changer for narrow tasks, they often require significant effort to refine their results, particularly when the user is not an expert in the topic. The conversation shifted to whether human reasoning is a stochastic or deterministic process, and whether or not punishing criminals in a deterministic universe can be justified.


Participants in the conversation agree that the brain receives incredible amounts of random input from its environment and that certain words can allow people to think differently. The discussion then shifts to the limitations of LLMs compared to living and capable learners. One commentator suggested improving data to make models more successful. Still, it’s agreed that until these models learn to say “I don’t know,” results have to be taken with caution.

The commenters’ dialogue raises critical questions regarding the ability of language models to replicate human reasoning. While models like GPT-4 have impressive capabilities, they lack the ability to learn continuously or understand complex concepts beyond probability calculations and language inputs. As such, they are not a replacement for human-level understanding or learning. Rather, they are an emerging technology that can be a valuable tool in specific use cases when used cautiously and accompanied by human expertise.

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