  We demonstrate the potential of the state-of-the-art OpenAI GPT-4 large
language model to engage in meaningful interactions with Astronomy papers using
in-context prompting. To optimize for efficiency, we employ a distillation
technique that effectively reduces the size of the original input paper by
50\%, while maintaining the paragraph structure and overall semantic integrity.
We then explore the model's responses using a multi-document context (ten
distilled documents). Our findings indicate that GPT-4 excels in the
multi-document domain, providing detailed answers contextualized within the
framework of related research findings. Our results showcase the potential of
large language models for the astronomical community, offering a promising
avenue for further exploration, particularly the possibility of utilizing the
models for hypothesis generation.
