@inproceedings{sbur-etal-2025-low,
    title = "A Low-Shot Prompting Approach to Lemmatization in the {E}va{C}un 2025 Shared Task",
    author = "Sbur, John  and
      Wilkins, Brandi  and
      Paul, Elizabeth  and
      Liu, Yudong",
    editor = "Anderson, Adam  and
      Gordin, Shai  and
      Li, Bin  and
      Liu, Yudong  and
      Passarotti, Marco C.  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the Second Workshop on Ancient Language Processing",
    month = may,
    year = "2025",
    address = "The Albuquerque Convention Center, Laguna",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.alp-1.31/",
    pages = "232--236",
    ISBN = "979-8-89176-235-0",
    abstract = "This study explores the use of low-shot prompt-ing techniques for the lemmatization of ancient cuneiform languages using Large Language Models (LLMs). To structure the input data and systematically design effective prompt tem-plates, we employed a hierarchical clustering approach based on Levenshtein distance The prompt design followed established engineer-ing patterns, incorporating instructional and response-guiding elements to enhance model comprehension. We employed the In-Context Learning (ICL) prompting strategy, selecting example words primarily based on lemma fre-quency, ensuring a balance between commonly occurring words and rare cases to improve gen-eralization. During testing on the develop-ment set, prompts included structured examples and explicit formatting rules, with accuracy assessed by comparing model predictions to ground truth lemmas. The results showed that model performance varied significantly across different configurations, with accuracy reach-ing approximately 90{\%} in the best case for in-vocabulary words and around 9{\%} in the best case for out-of-vocabulary (OOV) words. De-spite resource constraints and the lack of input from a language expert, oour findings suggest that prompt engineering strategies hold promise for improving LLM performance in cuneiform language lemmatization."
}