The article discusses a recent research paper titled “Large Language Model for Science: A Study on P vs. NP.” The paper explores the use of generative AI in solving the P vs. NP problem, which is considered the most important unsolved problem in computer science. The authors, including scientists from Microsoft, Peking University, Beihang University, and Beijing Technology and Business University, program OpenAI’s GPT-4 language model using a Socratic Method. By feeding arguments from a previous paper to GPT-4, the researchers prompt the model to provide useful responses.
The researchers found that GPT-4 offers arguments that suggest P does not equal NP, a significant finding in the field. This study demonstrates that large language models can go beyond generating text and potentially uncover new insights that may lead to scientific discoveries. The team refers to this potential as “LLMs for Science.” They conduct 97 prompt rounds, conditioning GPT-4 by assuming P equals NP and then finding ways to disprove it through proof by contradiction. The authors argue that their dialogue with the AI model showcases the capacity of GPT-4 to collaborate with humans in solving complex problems.
Overall, the research paper explores the integration of generative AI, specifically OpenAI’s GPT-4, in solving the P vs. NP problem. The authors demonstrate the ability of the language model to provide arguments suggesting that P does not equal NP. They suggest that this study highlights the potential of large language models in collaborating with humans to tackle complex problems and potentially make scientific breakthroughs. The findings contribute to the ongoing discussions surrounding the P vs. NP problem, which has implications for cryptography and quantum computing.
