When LLMs first appeared, people realised that you could ask them queries — for example, if you sent GPT-4 the prompt "What's the capital of France?", then it would continue with the word "Paris". That's because (1) GPT-4 is trained to be a good model of internet text, and (2) on the internet correct answers will often follow questions.

Unfortunately, this method will occasionally give you the wrong answer. That's because (1) GPT-4 is trained to be a good model of internet text, and (2) on the internet incorrect answers will also often follow questions. Recall that the internet doesn't just contain truths, it also contains common misconceptions, outdated information, lies, fiction, myths, jokes, memes, random strings, undeciphered logs, etc, etc.

Note that you will always achieve errors on the Q-and-A benchmarks when using LLMs with direct queries. That's true even in the limit of arbitrary compute, arbitrary data, and arbitrary algorithmic efficiency, because an LLM which perfectly models the internet will nonetheless return these commonly-stated incorrect answers. If you ask GPT-∞ "what's brown and sticky?", then it will reply "a stick", even though a stick isn't actually sticky.

In fact, the better the model, the more likely it is to repeat common misconceptions.