Large Language Models are Few-shot Publication Scoopers
This addresses a personal insecurity problem for researchers concerned about being beaten to breakthroughs, though it appears incremental in leveraging existing LLM trends.
The paper tackles the personal credit assignment problem in scientific discovery by proposing a pip-to-the-post algorithm that uses large language models to scoop groundbreaking findings, demonstrating its potential to secure adulatory Wikipedia pages without the risks of conventional research.
Driven by recent advances AI, we passengers are entering a golden age of scientific discovery. But golden for whom? Confronting our insecurity that others may beat us to the most acclaimed breakthroughs of the era, we propose a novel solution to the long-standing personal credit assignment problem to ensure that it is golden for us. At the heart of our approach is a pip-to-the-post algorithm that assures adulatory Wikipedia pages without incurring the substantial capital and career risks of pursuing high impact science with conventional research methodologies. By leveraging the meta trend of leveraging large language models for everything, we demonstrate the unparalleled potential of our algorithm to scoop groundbreaking findings with the insouciance of a seasoned researcher at a dessert buffet.