  This study investigates the application of Large Language Models (LLMs),
specifically GPT-4, within Astronomy. We employ in-context prompting, supplying
the model with up to 1000 papers from the NASA Astrophysics Data System, to
explore the extent to which performance can be improved by immersing the model
in domain-specific literature. Our findings point towards a substantial boost
in hypothesis generation when using in-context prompting, a benefit that is
further accentuated by adversarial prompting. We illustrate how adversarial
prompting empowers GPT-4 to extract essential details from a vast knowledge
base to produce meaningful hypotheses, signaling an innovative step towards
employing LLMs for scientific research in Astronomy.
