@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://aclanthology.org/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"
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<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</abstract>
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%0 Conference Proceedings
%T CRCL at SemEval-2024 Task 2: Simple prompt optimizations
%A Brutti-mairesse, Clement
%A Verlingue, Loic
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F brutti-mairesse-verlingue-2024-crcl
%X 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
%R 10.18653/v1/2024.semeval-1.67
%U https://aclanthology.org/2024.semeval-1.67/
%U https://doi.org/10.18653/v1/2024.semeval-1.67
%P 437-442
Markdown (Informal)
[CRCL at SemEval-2024 Task 2: Simple prompt optimizations](https://aclanthology.org/2024.semeval-1.67/) (Brutti-mairesse & Verlingue, SemEval 2024)
ACL