@inproceedings{brutti-mairesse-verlingue-2024-crcl,
    title = "{CRCL} at {S}em{E}val-2024 Task 2: Simple prompt optimizations",
    author = "Brutti-mairesse, Clement  and
      Verlingue, Loic",
    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/landing_page/2024.semeval-1.67/",
    doi = "10.18653/v1/2024.semeval-1.67",
    pages = "437--442",
    abstract = "We present a baseline for the SemEval 2024 task 2 challenge, whose objective is to ascertain the inference relationship between pairs of clinical trial report sections and statements.We apply prompt optimization techniques with LLM Instruct models provided as a Language Model-as-a-Service (LMaaS).We observed, in line with recent findings, that synthetic CoT prompts significantly enhance manually crafted ones.The source code is available at this GitHub repository https://github.com/ClementBM-CLB/semeval-2024"
}