@inproceedings{alzetta-etal-2024-similex,
    title = "{S}imil{E}x: The First {I}talian Dataset for Sentence Similarity with Natural Language Explanations",
    author = "Alzetta, Chiara  and
      Dell{'}orletta, Felice  and
      Fazzone, Chiara  and
      Venturi, Giulia",
    editor = "Dell'Orletta, Felice  and
      Lenci, Alessandro  and
      Montemagni, Simonetta  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
    month = dec,
    year = "2024",
    address = "Pisa, Italy",
    publisher = "CEUR Workshop Proceedings",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.clicit-1.4/",
    pages = "20--28",
    ISBN = "979-12-210-7060-6",
    abstract = "Large language models (LLMs) demonstrate great performance in natural language processing and understanding tasks. However, much work remains to enhance their interpretability. Annotated datasets with explanations could be key to addressing this issue, as they enable the development of models that provide human-like explanations for their decisions. In this paper, we introduce the SimilEx dataset, the first Italian dataset reporting human evaluations of similarity between pairs of sentences. For a subset of these pairs, the annotators also provided explanations in natural language for the scores assigned. The SimilEx dataset is valuable for exploring the variability in similarity perception between sentences and for training LLMs to offer human-like explanations for their predictions."
}