iML at SemEval-2024 Task 2: Safe Biomedical Natural Language Interference for Clinical Trials with LLM Based Ensemble Inferencing
Abbas Akkasi, Adnan Khan, Mai A. Shaaban, Majid Komeili, Mohammad Yaqub
Abstract
We engaged in the shared task 2 at SenEval-2024, employing a diverse set of solutions with a particular emphasis on leveraging a Large Language Model (LLM) based zero-shot inference approach to address the challenge.- Anthology ID:
- 2024.semeval-1.26
- 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:
- 170–174
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.26
- DOI:
- Cite (ACL):
- Abbas Akkasi, Adnan Khan, Mai A. Shaaban, Majid Komeili, and Mohammad Yaqub. 2024. iML at SemEval-2024 Task 2: Safe Biomedical Natural Language Interference for Clinical Trials with LLM Based Ensemble Inferencing. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 170–174, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- iML at SemEval-2024 Task 2: Safe Biomedical Natural Language Interference for Clinical Trials with LLM Based Ensemble Inferencing (Akkasi et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.26.pdf