@inproceedings{rawat-etal-2025-encoder,
    title = "Encoder Fine-tuning with Stochastic Sampling Outperforms Open-weight {GPT} in Astronomy Knowledge Extraction",
    author = "Rawat, Shivam  and
      Flek, Lucie  and
      Karimi, Akbar",
    editor = "Accomazzi, Alberto  and
      Ghosal, Tirthankar  and
      Grezes, Felix  and
      Lockhart, Kelly",
    booktitle = "Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications",
    month = dec,
    year = "2025",
    address = "Mumbai, India and virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.wasp-main.22/",
    pages = "195--200",
    ISBN = "979-8-89176-310-4",
    abstract = "Scientific literature in astronomy is rapidly expanding, making it increasingly important to automate the extraction of key entities and contextual information from research papers. In this paper, we present an encoder-based system for extracting knowledge from astronomy articles. Our objective is to develop models capable of classifying telescope references, detecting auxiliary semantic attributes, and recognizing instrument mentions from textual content. To this end, we implement a multi-task transformer-based system built upon the SciBERT model and fine-tuned for astronomy corpora classification. To carry out the fine-tuning, we stochastically sample segments from the training data and use majority voting over the test segments at inference time. Our system, despite its simplicity and low-cost implementation, significantly outperforms the open-weight GPT baseline."
}