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We demonstrate the potential of the state-of-the-art OpenAI GPT-4 largelanguage model to engage in meaningful interactions with Astronomy papers usingin-context prompting. To optimize for efficiency, we employ a distillationtechnique that effectively reduces the size of the original input paper by50\%, while maintaining the paragraph structure and overall semantic integrity.We then explore the model's responses using a multi-document context (tendistilled documents). Our findings indicate that GPT-4 excels in themulti-document domain, providing detailed answers contextualized within theframework of related research findings. Our results showcase the potential oflarge language models for the astronomical community, offering a promisingavenue for further exploration, particularly the possibility of utilizing themodels for hypothesis generation.

Galactic ChitChat: Using Large Language Models to Converse with Astronomy Literature