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From Queries to Criteria: Understanding How Astronomers Evaluate LLMs
Authors:
Alina Hyk,
Kiera McCormick,
Mian Zhong,
Ioana Ciucă,
Sanjib Sharma,
John F Wu,
J. E. G. Peek,
Kartheik G. Iyer,
Ziang Xiao,
Anjalie Field
Abstract:
There is growing interest in leveraging LLMs to aid in astronomy and other scientific research, but benchmarks for LLM evaluation in general have not kept pace with the increasingly diverse ways that real people evaluate and use these models. In this study, we seek to improve evaluation procedures by building an understanding of how users evaluate LLMs. We focus on a particular use case: an LLM-po…
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There is growing interest in leveraging LLMs to aid in astronomy and other scientific research, but benchmarks for LLM evaluation in general have not kept pace with the increasingly diverse ways that real people evaluate and use these models. In this study, we seek to improve evaluation procedures by building an understanding of how users evaluate LLMs. We focus on a particular use case: an LLM-powered retrieval-augmented generation bot for engaging with astronomical literature, which we deployed via Slack. Our inductive coding of 368 queries to the bot over four weeks and our follow-up interviews with 11 astronomers reveal how humans evaluated this system, including the types of questions asked and the criteria for judging responses. We synthesize our findings into concrete recommendations for building better benchmarks, which we then employ in constructing a sample benchmark for evaluating LLMs for astronomy. Overall, our work offers ways to improve LLM evaluation and ultimately usability, particularly for use in scientific research.
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Submitted 5 August, 2025; v1 submitted 21 July, 2025;
originally announced July 2025.
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pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
Authors:
Kartheik G. Iyer,
Mikaeel Yunus,
Charles O'Neill,
Christine Ye,
Alina Hyk,
Kiera McCormick,
Ioana Ciuca,
John F. Wu,
Alberto Accomazzi,
Simone Astarita,
Rishabh Chakrabarty,
Jesse Cranney,
Anjalie Field,
Tirthankar Ghosal,
Michele Ginolfi,
Marc Huertas-Company,
Maja Jablonska,
Sandor Kruk,
Huiling Liu,
Gabriel Marchidan,
Rohit Mistry,
J. P. Naiman,
J. E. G. Peek,
Mugdha Polimera,
Sergio J. Rodriguez
, et al. (5 additional authors not shown)
Abstract:
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords.…
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The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature.
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Submitted 2 August, 2024;
originally announced August 2024.
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Designing an Evaluation Framework for Large Language Models in Astronomy Research
Authors:
John F. Wu,
Alina Hyk,
Kiera McCormick,
Christine Ye,
Simone Astarita,
Elina Baral,
Jo Ciuca,
Jesse Cranney,
Anjalie Field,
Kartheik Iyer,
Philipp Koehn,
Jenn Kotler,
Sandor Kruk,
Michelle Ntampaka,
Charles O'Neill,
Joshua E. G. Peek,
Sanjib Sharma,
Mikaeel Yunus
Abstract:
Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them. However, there is currently no standard for evaluating the use of LLMs in astronomy. Therefore, we present the experimental design for an evaluation study on how astronomy rese…
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Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them. However, there is currently no standard for evaluating the use of LLMs in astronomy. Therefore, we present the experimental design for an evaluation study on how astronomy researchers interact with LLMs. We deploy a Slack chatbot that can answer queries from users via Retrieval-Augmented Generation (RAG); these responses are grounded in astronomy papers from arXiv. We record and anonymize user questions and chatbot answers, user upvotes and downvotes to LLM responses, user feedback to the LLM, and retrieved documents and similarity scores with the query. Our data collection method will enable future dynamic evaluations of LLM tools for astronomy.
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Submitted 30 May, 2024;
originally announced May 2024.