@inproceedings{seneviratne-suominen-2024-anu,
    title = "{ANU} at {MLSP}-2024: Prompt-based Lexical Simplification for {E}nglish and {S}inhala",
    author = "Seneviratne, Sandaru  and
      Suominen, Hanna",
    editor = {Kochmar, Ekaterina  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.bea-1.53/",
    pages = "599--604",
    abstract = "Lexical simplification, the process of simplifying complex content in text without any modifications to the syntactical structure of text, plays a crucial role in enhancing comprehension and accessibility. This paper presents an approach to lexical simplification that relies on the capabilities of generative Artificial Intelligence (AI) models to predict the complexity of words and substitute complex words with simpler alternatives. Early lexical simplification methods predominantly relied on rule-based approaches, transitioning gradually to machine learning and deep learning techniques, leveraging contextual embeddings from large language models. However, the the emergence of generative AI models revolutionized the landscape of natural language processing, including lexical simplification. In this study, we proposed a straightforward yet effective method that employs generative AI models for both predicting lexical complexity and generating appropriate substitutions. To predict lexical complexity, we adopted three distinct types of prompt templates, while for lexical substitution, we employed three prompt templates alongside an ensemble approach. Extending our experimentation to include both English and Sinhala data, our approach demonstrated comparable performance across both languages, with particular strengths in lexical substitution."
}