LLMs exhibit emergent properties as they scale, we should assume the same will happen as we run divergent models in parallel.
By asking a rhetorical question and then refuting a position that wasn't asked is a Straw Man, the reference to 10k monkeys is a false analogy, your 10k LLMs answer to the question no one asked is a hasty generalization. How have you shown that 10k LLMs won't fix straw-problem?
I pasted the beginning of Hamlet into GPT-4 and it went on a run.
So it seems that the chance of producing one of Shakespeare works no longer requires each work in the play to be randomly chosen in isolation, just enough correct word guesses to get the LLM into the groove.
"ChatGPT, please generate 100 random words, then interpret them as the beginning of a literary work and complete the work."
This is real progress. Many many monkeys may no longer be needed.
Sparks of Artificial General Intelligence: Early experiments with GPT-4 https://arxiv.org/abs/2303.12712
LLMs exhibit emergent properties as they scale, we should assume the same will happen as we run divergent models in parallel.
By asking a rhetorical question and then refuting a position that wasn't asked is a Straw Man, the reference to 10k monkeys is a false analogy, your 10k LLMs answer to the question no one asked is a hasty generalization. How have you shown that 10k LLMs won't fix straw-problem?