Abstract
In this work, we (team RGAT) describe our approaches for the SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials (NLI4CT). The objective of this task is multi-evidence natural language inference based on different sections of clinical trial reports. We have explored various approaches, (a) dependency tree of the input query as additional features in a Graph Attention Network (GAT) along with the token and parts-of-speech features, (b) sequence-to-sequence approach using various models and synthetic data and finally, (c) in-context learning using large language models (LLMs) like GPT-4. Amongs these three approaches the best result is obtained from the LLM with 0.76 F1-score (the highest being 0.78), 0.86 in faithfulness and 0.74 in consistence.- Anthology ID:
- 2024.semeval-1.19
- 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:
- 116–122
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.19
- DOI:
- Cite (ACL):
- Abir Chakraborty. 2024. RGAT at SemEval-2024 Task 2: Biomedical Natural Language Inference using Graph Attention Network. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 116–122, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- RGAT at SemEval-2024 Task 2: Biomedical Natural Language Inference using Graph Attention Network (Chakraborty, SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.19.pdf