Rethinking Domain Specialization for Open-Ended Scientific Reasoning in Astronomy Language Models

This research evaluates the efficacy of domain-specialized language models (LMs) for open-ended scientific reasoning in astronomy, specifically addressing whether fine-tuning remains beneficial given the advancements in general-purpose LMs. Using a curated benchmark of 300 astronomy-related questions (204 text-only, 96 image-linked) from 2017-2026 Olympiad materials, the study compares general-purpose, multimodal, and astronomy-specialized models. Initial findings indicate that robust general-purpose models currently establish the highest correctness baseline in this test environment.

This research provides critical insights into the evolving landscape of artificial intelligence application in specialized fields. Understanding the comparative performance of general versus domain-specific models can inform strategic investments in AI development, training data curation, and the deployment of AI tools across various sectors requiring complex reasoning, beyond just scientific domains.

The study questions the ongoing value of domain-specific fine-tuning for scientific reasoning in language models, particularly with the emergence of stronger general-purpose systems.

Forward consideration, not a verified fact.