msu-ceco/agxqa_v1

Tasks

• question-answering

Citation text from the card

@article{KPODO2024109349,
    title = {AgXQA: A benchmark for advanced Agricultural Extension question answering},
    journal = {Computers and Electronics in Agriculture},
    volume = {225},
    pages = {109349},
    year = {2024},
    issn = {0168-1699},
    doi = {https://doi.org/10.1016/j.compag.2024.109349},
    url = {https://www.sciencedirect.com/science/article/pii/S0168169924007403},
    author = {Josué Kpodo and Parisa Kordjamshidi and A. Pouyan Nejadhashemi},
    keywords = {Agricultural Extension, Question-Answering, Annotated Dataset, Large Language Models, Zero-Shot Learning},
    abstract = {Large language models (LLMs) have revolutionized various scientific fields in the past few years, thanks to their generative and extractive abilities. However, their applications in the Agricultural Extension (AE) domain remain sparse and limited due to the unique challenges of unstructured agricultural data. Furthermore, mainstream LLMs excel at general and open-ended tasks but struggle with domain-specific tasks. We proposed a novel QA benchmark dataset, AgXQA, for the AE domain to address these issues. We trained and evaluated our domain-specific LM, AgRoBERTa, which outperformed other mainstream encoder- and decoder- LMs, on the extractive QA downstream task by achieving an EM score of 55.15% and an F1 score of 78.89%. Besides automated metrics, we also introduced a custom human evaluation metric, AgEES, which confirmed AgRoBERTa’s performance, as demonstrated by a 94.

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