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- Anthology ID:
- 2024.semeval-1.67
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 437–442
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.67
- DOI:
- Cite (ACL):
- Clement Brutti-mairesse and Loic Verlingue. 2024. CRCL at SemEval-2024 Task 2: Simple prompt optimizations. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 437–442, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- CRCL at SemEval-2024 Task 2: Simple prompt optimizations (Brutti-mairesse & Verlingue, SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.67.pdf