Communications of the ACM
Can Generative AI Solve Computer Science's Greatest Unsolved Problem?
The team's framework encourages LLMs to recursively discover, solve, and integrate problems while facilitating self-evaluation and refinement.
Credit: Getty Images
Does P = NP? Formulated nearly 50 years ago, the question is a deep meditation on what can ultimately be achieved with computers, and has resisted a convincing answer despite decades of intense study. Now, that effort has enlisted the help of generative AI.
In "Large Language Model for Science: A Study on P vs. NP," lead author Qingxiu Dong and colleagues program OpenAI's GPT-4 large language model using what they call a Socratic Method, several turns of chat via prompt with GPT-4.
Dong and team claim that the work shows that large language models can "discover novel insights" that may lead to "scientific discoveries," a prospect they christen "LLMs for Science."
Through 97 prompt rounds, the authors coax GPT-4 with a variety of requests that get into the nitty-gritty of the mathematics of P = NP, prepending each of their prompts with a leading statement to condition GPT-4, such as, "You are a wise philosopher," "You are a mathematician skilled in probability theory" — in other words, the now familiar game of getting GPT-4 to play a role, or, "persona" to stylize its text generation.
Their strategy is to induce GPT-4 to prove that P does not, in fact, equal NP, by first assuming that it does with an example and then finding a way that the example falls apart — an approach known as proof by contradiction.
From ZDNET
View Full Article
No entries found