Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2023
Abstract
Few-shot or zero-shot fact verification only relies on a few or no labeled training examples. In this paper, we propose a novel method called ProToCo, to Prompt pre-trained language models (PLMs) To be Consistent, for improving the factuality assessment capability of PLMs in the few-shot and zero-shot settings. Given a claim-evidence pair, ProToCo generates multiple variants of the claim with different relations and frames a simple consistency mechanism as constraints for making compatible predictions across these variants. We update PLMs by using parameter-efficient fine-tuning (PEFT), leading to more accurate predictions in few-shot and zero-shot fact verification tasks. Our experiments on three public verification datasets show that ProToCo significantly outperforms state-of-the-art few-shot fact verification baselines. With a small number of unlabeled instances, ProToCo also outperforms the strong zero-shot learner T0 on zero-shot verification. Compared to large PLMs using in-context learning (ICL) method, ProToCo outperforms OPT-30B and the Self-Consistency-enabled OPT-6.7B model in both few- and zero-shot settings.
Keywords
few-shot fact verification, zero-shot fact verification, ProToCo, Prompt pre-trained language models, factuality assessment, claim-evidence pair, consistency mechanism, variants, predictions, parameter-efficient fine-tuning (PEFT), accurate predictions, public verification datasets, zero-shot learner, in-context learning (ICL), OPT-30B, Self-Consistency-enabled OPT-6.7B model
Discipline
Computer Sciences
Research Areas
Data Science and Engineering
Publication
Findings of the Association for Computational Linguistics: ACL 2023
First Page
4555
Last Page
4569
Identifier
10.18653/v1/2023.findings-acl.278
Publisher
Association for Computational Linguistics
City or Country
USA
Citation
ZENG, Fengzhu and GAO, Wei.
Prompt to be consistent is better than self-consistent? Few-shot and zero-shot fact verification with pre-trained language models. (2023). Findings of the Association for Computational Linguistics: ACL 2023. 4555-4569.
Available at: https://ink.library.smu.edu.sg/sis_research/8452
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.18653/v1/2023.findings-acl.278