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.

翻译：本研究探讨了大语言模型（LLMs），特别是GPT-4，在天文学中的应用。我们采用上下文提示方法，向模型提供来自NASA天体物理学数据系统的高达1000篇论文，以探究将模型沉浸于领域特定文献中可在多大程度上提升其性能。研究结果表明，使用上下文提示能显著增强假设生成能力，而对抗提示则可进一步放大这一优势。我们展示了对抗提示如何使GPT-4从庞大的知识库中提取关键细节，从而生成有意义的假设，这标志着将大语言模型应用于天文学科学研究迈出了创新性的一步。