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What is the Role of Large Language Models in the Evolution of Astronomy Research?

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arxiv 2409.20252 v2 pith:4BPAAULI submitted 2024-09-30 astro-ph.IM cs.AI

classification astro-ph.IMcs.AI
keywords llmsresearchmodelstasksapplicationsfieldslanguagelarge
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ChatGPT and other state-of-the-art large language models (LLMs) are rapidly transforming multiple fields, offering powerful tools for a wide range of applications. These models, commonly trained on vast datasets, exhibit human-like text generation capabilities, making them useful for research tasks such as ideation, literature review, coding, drafting, and outreach. We conducted a study involving 13 astronomers at different career stages and research fields to explore LLM applications across diverse tasks over several months and to evaluate their performance in research-related activities. This work was accompanied by an anonymous survey assessing participants' experiences and attitudes towards LLMs. We provide a detailed analysis of the tasks attempted and the survey answers, along with specific output examples. Our findings highlight both the potential and limitations of LLMs in supporting research while also addressing general and research-specific ethical considerations. We conclude with a series of recommendations, emphasizing the need for researchers to complement LLMs with critical thinking and domain expertise, ensuring these tools serve as aids rather than substitutes for rigorous scientific inquiry.

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Cited by 3 Pith papers

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  2. How to Craft the Right Language AI Policy For Your Research Group (Some Assembly Required)

    astro-ph.IM 2026-07 accept novelty 6.0 of 10

    There is no one-size-fits-all AI policy for astronomy groups; four value-based archetypes help labs design rules matched to their own priorities.

  3. From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines

    cs.DL 2026-06 unverdicted novelty 3.0 of 10

    LLMs accelerate research workflows from idea generation to writing but introduce challenges like hallucination, bias, opacity, and ten systemic risks requiring new governance frameworks.

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