Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models
Majid Zarharan, Pascal Wullschleger, Babak Behkam Kia, Mohammad Taher Pilehvar, Jennifer Foster
Abstract
This paper presents a comprehensive analysis of explainable fact-checking through a series of experiments, focusing on the ability of large language models to verify public health claims and provide explanations or justifications for their veracity assessments. We examine the effectiveness of zero/few-shot prompting and parameter-efficient fine-tuning across various open and closed-source models, examining their performance in both isolated and joint tasks of veracity prediction and explanation generation. Importantly, we employ a dual evaluation approach comprising previously established automatic metrics and a novel set of criteria through human evaluation. Our automatic evaluation indicates that, within the zero-shot scenario, GPT-4 emerges as the standout performer, but in few-shot and parameter-efficient fine-tuning contexts, open-source models demonstrate their capacity to not only bridge the performance gap but, in some instances, surpass GPT-4. Human evaluation reveals yet more nuance as well as indicating potential problems with the gold explanations.- Anthology ID:
- 2024.trustnlp-1.21
- Volume:
- Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kai-Wei Chang, Anaelia Ovalle, Jieyu Zhao, Yang Trista Cao, Ninareh Mehrabi, Aram Galstyan, Jwala Dhamala, Anoop Kumar, Rahul Gupta
- Venues:
- TrustNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 252–278
- Language:
- URL:
- https://aclanthology.org/2024.trustnlp-1.21
- DOI:
- Cite (ACL):
- Majid Zarharan, Pascal Wullschleger, Babak Behkam Kia, Mohammad Taher Pilehvar, and Jennifer Foster. 2024. Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models. In Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024), pages 252–278, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models (Zarharan et al., TrustNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.trustnlp-1.21.pdf