Can large language models solve logical puzzles? To find out, Fernando Perez-Cruz and Hyun Song Shin asked GPT-4. Shin, head of research at the Bank for International Settlements, brought up the puzzle "Cheryl's Birthday," where Albert and Bernard have to guess Cheryl's birthday based on hints she gives. After some consideration, both can determine the date. But that wasn't the real test. The researchers changed the names and months in the puzzle, and GPT-4 failed to solve the modified version correctly, even though it explained the original puzzle masterfully.

This shows that while GPT-4 may sound logical and convincing, it often merely provides familiar answers without truly understanding the logic. This illusion of brilliance carries risks when it comes to important decisions.

Another example is the Monty Hall problem, in which a contestant has to choose between three doors, one of which conceals a prize. The quizmaster opens a door without a prize and offers the contestant the option to switch their choice. The correct strategy is to switch doors, but when Perez-Cruz posed the puzzle with additional complications, GPT-4 again made errors, despite correctly explaining the basics.

These examples illustrate a fundamental problem: Large language models like GPT-4 are amazingly good at generating plausible but incorrect answers. They can appear convincing even when they are wrong, which makes their use risky. As Perez-Cruz and Shin emphasize, a flawed language model could have fatal consequences if utilized in critical decision-making processes.

A language model that seems so right but is actually so wrong is a dangerous weapon. It is like relying on a spreadsheet that occasionally forgets how multiplication works. These insights should serve as a warning to use language models with caution and to always critically evaluate their responses.