LLMs Don’t Think. They Just Get Lucky.
Author(s): Devrim Ozcay
Originally published on Towards AI.
LLMs Don’t Think. They Just Get Lucky.
I spent six months feeding GPT-4 the same prompt 10,000 times and got 10,000 different answers. Not slightly different. Completely different. Same temperature. Same parameters. Same everything.
The article discusses the limits of Large Language Models (LLMs), arguing that they operate on complex patterns rather than actual understanding or reasoning. The author highlights experiments showing LLMs struggle with consistency and can produce wildly differing results to the same prompt. As a caution against overestimating AI capabilities, they point out that LLMs are good at matching templates found in training data but fail in unpredictable situations, warning that relying on them as intelligent agents is a mistake. The piece concludes by urging readers to reconsider their expectations of LLMs and to treat them as valuable but ultimately limited tools.
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