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.

翻译：我们展示了最先进的OpenAI GPT-4大型语言模型通过上下文提示与天文学论文进行有意义交互的潜力。为优化效率，我们采用了一种蒸馏技术，在保持段落结构和整体语义完整性的前提下，将原始输入论文的规模有效缩减50%。随后，我们通过多文档上下文（十篇蒸馏文档）探索模型的响应能力。研究结果表明，GPT-4在多文档领域表现卓越，能提供与相关研究框架背景化的详细答案。我们的成果展示了大型语言模型在天文学界的应用潜力，为后续探索提供了前景广阔的方向，特别是利用模型进行假设生成的可能性。