We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 multiple-choice questions curated from the Annual Review of Astronomy and Astrophysics, covering a broad range of astrophysical topics. Our analysis examines model performance across various astronomical subfields and assesses response calibration, crucial for potential deployment in research environments. Claude-3.5-Sonnet outperforms competitors by up to 4.6 percentage points, achieving 85.0% accuracy. For proprietary models, we observed a universal reduction in cost every 3-to-12 months to achieve similar score in this particular astronomy benchmark. Open-source models have rapidly improved, with LLaMA-3-70b (80.6%) and Qwen-2-72b (77.7%) now competing with some of the best proprietary models. We identify performance variations across topics, with non-English-focused models generally struggling more in exoplanet-related fields, stellar astrophysics, and instrumentation related questions. These challenges likely stem from less abundant training data, limited historical context, and rapid recent developments in these areas. This pattern is observed across both open-weights and proprietary models, with regional dependencies evident, highlighting the impact of training data diversity on model performance in specialized scientific domains. Top-performing models demonstrate well-calibrated confidence, with correlations above 0.9 between confidence and correctness, though they tend to be slightly underconfident. The development for fast, low-cost inference of open-weights models presents new opportunities for affordable deployment in astronomy. The rapid progress observed suggests that LLM-driven research in astronomy may become feasible in the near future.

To enhance this, we introduce AstroLLaMA-Chat, an advanced version of AstroLLaMA. This new iteration broadens the training scope to include introductions and conclusions of papers, alongside abstracts, as these sections are often rich in pivotal information for question-answering tasks. We initiated by downloading all papers up to July 2023, including all the files that come with a submission to arXiv. The data has been further refined for optimal operability, retaining only files with ".tex" suffixes. Through a multi-stage process, and utilising a comprehensive regex matching process, the extraction of the targeted sections was performed. Given the diverse LaTeX formatting standards, approximately 90% of the samples remained post-processing. Subsequently, we removed specific formatting patterns, comments, and superfluous symbols like newlines to ensure the readability of the training data. Further, we have fine-tuned AstroLLaMA-Chat on a domain-specific dialogue dataset. To generate Question-Answer pairs, we engaged GPT-4 (OpenAI 2023) to formulate pertinent questions from paragraphs within 300,000 arXiv papers, with GPT-4 also tasked with answering these questions by retrieving context-relevant information.