@inproceedings{venkatesh-raman-2024-bits,
    title = "{BITS} Pilani at {S}em{E}val-2024 Task 1: Using text-embedding-3-large and {L}a{BSE} embeddings for Semantic Textual Relatedness",
    author = "Venkatesh, Dilip  and
      Raman, Sundaresan",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Tayyar Madabushi, Harish  and
      Da San Martino, Giovanni  and
      Rosenthal, Sara  and
      Ros{\'a}, Aiala},
    booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.semeval-1.124/",
    doi = "10.18653/v1/2024.semeval-1.124",
    pages = "865--868",
    abstract = "Semantic Relatedness of a pair of text (sentences or words) is the degree to which theirmeanings are close. The Track A of the Semantic Textual Relatedness shared task aimsto find the semantic relatedness for the English language along with multiple other lowresource languages with the use of pretrainedlanguage models. We proposes a system tofind the Spearman coefficient of a textual pairusing pretrained embedding models like textembedding-3-large and LaBSE."
}