Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2023

Abstract

While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still underexplored in the misinformation domain. In this paper, we examine LLMs with in-context learning (ICL) for news claim verification, and find that only with 4-shot demonstration examples, the performance of several prompting methods can be comparable with previous supervised models. To further boost performance, we introduce a Hierarchical Step-by-Step (HiSS) prompting method which directs LLMs to separate a claim into several subclaims and then verify each of them via multiple questionsanswering steps progressively. Experiment results on two public misinformation datasets show that HiSS prompting outperforms stateof-the-art fully-supervised approach and strong few-shot ICL-enabled baselines.

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Data Science and Engineering

Publication

Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, Bali, Indonesia, November 1-4

First Page

996

Last Page

1011

Publisher

Association for Computational Linguistics

City or Country

New York, USA

Copyright Owner and License

Authors

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