Designing an Evaluation Framework for Large Language Models in Astronomy Research
Abstrak
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.
Topik & Kata Kunci
Penulis (18)
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
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓