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

arXiv: https://arxiv.org/abs/2409.20252 · HTML: https://mlsanity.com/paper/2409.20252/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Does Writing with Language Models Reduce Content Diversity?](https://mlsanity.com/paper/2309.05196/) (2023)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [Is ChatGPT Transforming Academics' Writing Style?](https://mlsanity.com/paper/2404.08627/) (2024)
- [Challenging the appearance of machine intelligence: Cognitive bias in LLMs and Best Practices for Adoption](https://mlsanity.com/paper/2304.01358/) (2023)
- [Women Through the Glass Ceiling: Gender Asymmetries in Wikipedia](https://mlsanity.com/paper/1601.04890/) (2016)
- [Friend or Foe? Exploring the Implications of Large Language Models on the Science System](https://mlsanity.com/paper/2306.09928/) (2023)
- [Intent-based Prompt Calibration: Enhancing prompt optimization with synthetic boundary cases](https://mlsanity.com/paper/2402.03099/) (2024)
- [Delving into the Utilisation of ChatGPT in Scientific Publications in Astronomy](https://mlsanity.com/paper/2406.17324/) (2024)
- [pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy](https://mlsanity.com/paper/2408.01556/) (2024)
- [Galactic ChitChat: Using Large Language Models to Converse with Astronomy Literature](https://mlsanity.com/paper/2304.05406/) (2023)
- [Gender Differences in Participation and Reward on Stack Overflow](https://mlsanity.com/paper/1811.11539/) (2018)
- [Software Use in Astronomy: an Informal Survey](https://mlsanity.com/paper/1507.03989/) (2015)

## Built on by (most cited first, 4 of 4)

- [InferA: A Smart Assistant for Cosmological Ensemble Data](https://mlsanity.com/paper/2510.12920/) (2025)
- [T-FIX: Text-Based Explanations with Features Interpretable to eXperts](https://mlsanity.com/paper/2511.04070/) (2025)
- [Evaluating Retrieval-Augmented Generation Agents for Autonomous Scientific Discovery in Astrophysics](https://mlsanity.com/paper/2507.07155/) (2025)
- [amc: The Automated Mission Classifier for Telescope Bibliographies](https://mlsanity.com/paper/2512.11202/) (2025)

## Abstract (arXiv)

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.