@inproceedings{liu-etal-2025-variable,
    title = "Variable Extraction for Model Recovery in Scientific Literature",
    author = "Liu, Chunwei  and
      Noriega-Atala, Enrique  and
      Pyarelal, Adarsh  and
      Morrison, Clayton T  and
      Cafarella, Mike",
    editor = "Jansen, Peter  and
      Dalvi Mishra, Bhavana  and
      Trivedi, Harsh  and
      Prasad Majumder, Bodhisattwa  and
      Hope, Tom  and
      Khot, Tushar  and
      Downey, Doug  and
      Horvitz, Eric",
    booktitle = "Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.aisd-main.1/",
    pages = "1--12",
    ISBN = "979-8-89176-224-4",
    abstract = "Due to the increasing productivity in the scientific community, it is difficult to keep up with the literature without the assistance of AI methods. This paper evaluates various methods for extracting mathematical model variables from epidemiological studies, such as `infection rate ($\alpha$),'' `recovery rate ($\gamma$),'' and `mortality rate ($\mu$).'' Variable extraction appears to be a basic task, but plays a pivotal role in recovering models from scientific literature. Once extracted, we can use these variables for automatic mathematical modeling, simulation, and replication of published results. We also introduce a benchmark dataset comprising manually-annotated variable descriptions and variable values extracted from scientific papers. Our analysis shows that LLM-based solutions perform the best. Despite the incremental benefits of combining rule-based extraction outputs with LLMs, the leap in performance attributed to the transfer-learning and instruction-tuning capabilities of LLMs themselves is far more significant. This investigation demonstrates the potential of LLMs to enhance automatic comprehension of scientific artifacts and for automatic model recovery and simulation."
}