Abstract
Claim verification plays a crucial role in combating misinformation. While existing works on claim verification have shown promising results, a crucial piece of the puzzle that remains unsolved is to understand how to verify claims without relying on human-annotated data, which is expensive to create at a large scale. Additionally, it is important for models to provide comprehensive explanations that can justify their decisions and assist human fact-checkers. This paper presents First-Order-Logic-Guided Knowledge-Grounded (FOLK) Reasoning that can verify complex claims and generate explanations without the need for annotated evidence using Large Language Models (LLMs). FOLK leverages the in-context learning ability of LLMs to translate the claim into a First-Order-Logic (FOL) clause consisting of predicates, each corresponding to a sub-claim that needs to be verified. Then, FOLK performs FOL-Guided reasoning over a set of knowledge-grounded question-and-answer pairs to make veracity predictions and generate explanations to justify its decision-making process. This process makes our model highly explanatory, providing clear explanations of its reasoning process in human-readable form. Our experiment results indicate that FOLK outperforms strong baselines on three datasets encompassing various claim verification challenges. Our code and data are available.- Anthology ID:
- 2023.findings-emnlp.416
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6288–6304
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.416
- DOI:
- 10.18653/v1/2023.findings-emnlp.416
- Cite (ACL):
- Haoran Wang and Kai Shu. 2023. Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6288–6304, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models (Wang & Shu, Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-emnlp.416.pdf