@inproceedings{muntean-betts-2025-addressing,
    title = "Addressing Few-Shot {LLM} Classification Instability Through Explanation-Augmented Distillation",
    author = "Muntean, William  and
      Betts, Joe",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress",
    month = oct,
    year = "2025",
    address = "Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.aimecon-wip.24/",
    pages = "197--203",
    ISBN = "979-8-218-84229-1",
    abstract = "This study compares explanation-augmented knowledge distillation with few-shot in-context learning for LLM-based exam question classification. Fine-tuned smaller language models achieved competitive performance with greater consistency than large mode few-shot approaches, which exhibited notable variability across different examples. Hyperparameter selection proved essential, with extremely low learning rates significantly impairing model performance."
}