Problem
Research questions and friction points this paper is trying to address.
Domain Specialization
Open-Ended Scientific Reasoning
Astronomy Language Models
Rethinking Domain Specialization for Open-Ended Scientific Reasoning in Astronomy Language Models
🤖 AI Summary
研究探讨了在开放性科学推理中，何时需要领域特定微调，并通过天文学问题集对比通用和领域特化模型的效果。
📝 Abstract
Domain-specialized language models are widely used for scientific question answering, but stronger general-purpose systems raise a sharper question: when does domain-specific fine-tuning remain valuable for open-ended scientific reasoning? We study this in astronomy with a curated QA benchmark from publicly available 2017--2026 Olympiad-style materials. The free-response subset contains 300 questions, including 204 text-only and 96 image-linked examples. We compare open-weight and API-served general-purpose, multimodal, and astronomy-specialized models using judge-based correctness and complementary reference metrics. Strong general-purpose models establish the highest correctness baseline in this testbed, while analyses of metric agreement, judge sensitivity, benchmark composition, and modality reveal variation not captured by a single leaderboard. These results motivate treating domain specialization as a task- and deployment-dependent property and highlight the role of domain-specific evaluation in determining which models, capabilities, and evaluation criteria are appropriate for scientific workflows.
Innovation
Methods, ideas, or system contributions that make the work stand out.
domain specialization
open-ended scientific reasoning
astronomy language models
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