@inproceedings{modesitt-etal-2025-orbit,
title = "{ORBIT}: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study",
author = "Modesitt, Eric and
Yang, Ke and
Hulsey, Spencer and
Liu, Xin and
Zhai, ChengXiang and
Kindratenko, Volodymyr",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.51/",
doi = "10.18653/v1/2025.findings-acl.51",
pages = "907--926",
ISBN = "979-8-89176-256-5",
abstract = "Recent advances in language modeling demonstrate the need for high-quality domain-specific training data, especially for tasks that require specialized knowledge. General-purpose models, while versatile, often lack the depth needed for expert-level tasks because of limited domain-specific information. Domain adaptation training can enhance these models, but it demands substantial, high-quality data. To address this, we propose ORBIT, a cost-efficient methodology for curating massive, high-quality domain-specific datasets from noisy web sources, tailored for training specialist large language models. Using astronomy as a primary case study, we refined the 1.3T-token FineWeb-Edu dataset into a high-quality, 10B-token subset focused on astronomy. Fine-tuning LLaMA-3-8B on a 1B-token astronomy subset improved performance on the MMLU astronomy benchmark from 69{\%} to 76{\%} and achieved top results on AstroBench, an astronomy-specific benchmark. Moreover, our model (Orbit-LLaMA) outperformed LLaMA-3-8B-base, with GPT-4o evaluations preferring it in 73{\%} of cases across 1000 astronomy-specific questions. Additionally, we validated ORBIT{'}s generalizability by applying it to law and medicine, achieving a significant improvement of data quality compared to an unfiltered baseline. We open-source the ORBIT methodology, including the curated datasets, the codebase, and the resulting model."
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<abstract>Recent advances in language modeling demonstrate the need for high-quality domain-specific training data, especially for tasks that require specialized knowledge. General-purpose models, while versatile, often lack the depth needed for expert-level tasks because of limited domain-specific information. Domain adaptation training can enhance these models, but it demands substantial, high-quality data. To address this, we propose ORBIT, a cost-efficient methodology for curating massive, high-quality domain-specific datasets from noisy web sources, tailored for training specialist large language models. Using astronomy as a primary case study, we refined the 1.3T-token FineWeb-Edu dataset into a high-quality, 10B-token subset focused on astronomy. Fine-tuning LLaMA-3-8B on a 1B-token astronomy subset improved performance on the MMLU astronomy benchmark from 69% to 76% and achieved top results on AstroBench, an astronomy-specific benchmark. Moreover, our model (Orbit-LLaMA) outperformed LLaMA-3-8B-base, with GPT-4o evaluations preferring it in 73% of cases across 1000 astronomy-specific questions. Additionally, we validated ORBIT’s generalizability by applying it to law and medicine, achieving a significant improvement of data quality compared to an unfiltered baseline. We open-source the ORBIT methodology, including the curated datasets, the codebase, and the resulting model.</abstract>
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%0 Conference Proceedings
%T ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study
%A Modesitt, Eric
%A Yang, Ke
%A Hulsey, Spencer
%A Liu, Xin
%A Zhai, ChengXiang
%A Kindratenko, Volodymyr
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F modesitt-etal-2025-orbit
%X Recent advances in language modeling demonstrate the need for high-quality domain-specific training data, especially for tasks that require specialized knowledge. General-purpose models, while versatile, often lack the depth needed for expert-level tasks because of limited domain-specific information. Domain adaptation training can enhance these models, but it demands substantial, high-quality data. To address this, we propose ORBIT, a cost-efficient methodology for curating massive, high-quality domain-specific datasets from noisy web sources, tailored for training specialist large language models. Using astronomy as a primary case study, we refined the 1.3T-token FineWeb-Edu dataset into a high-quality, 10B-token subset focused on astronomy. Fine-tuning LLaMA-3-8B on a 1B-token astronomy subset improved performance on the MMLU astronomy benchmark from 69% to 76% and achieved top results on AstroBench, an astronomy-specific benchmark. Moreover, our model (Orbit-LLaMA) outperformed LLaMA-3-8B-base, with GPT-4o evaluations preferring it in 73% of cases across 1000 astronomy-specific questions. Additionally, we validated ORBIT’s generalizability by applying it to law and medicine, achieving a significant improvement of data quality compared to an unfiltered baseline. We open-source the ORBIT methodology, including the curated datasets, the codebase, and the resulting model.
%R 10.18653/v1/2025.findings-acl.51
%U https://aclanthology.org/2025.findings-acl.51/
%U https://doi.org/10.18653/v1/2025.findings-acl.51
%P 907-926
Markdown (Informal)
[ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study](https://aclanthology.org/2025.findings-acl.51/) (Modesitt et al., Findings 2025)
ACL