Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2024

Abstract

Learning multi-task models for jointly detecting stance and verifying rumors poses challenges due to the need for training data of stance at post level and rumor veracity at claim level, which are difficult to obtain. To address this issue, we leverage large language models (LLMs) as the foundation annotators for the joint stance detection (SD) and rumor verification (RV) tasks, dubbed as JSDRV. We introduce a novel reinforcement tuning framework to enhance the joint predictive capabilities of LLM-based SD and RV components. Specifically, we devise a policy for selecting LLM-annotated data at the two levels, employing a hybrid reward mechanism to choose high-quality labels for effective LLM fine-tuning on both tasks. Results demonstrate that JSDRV improves the capabilities of LLMs in the joint tasks, not only outperforming state-of-the-art methods but also generalizing to non-LLMs accommodated as task models.

Discipline

Databases and Information Systems | Programming Languages and Compilers

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

Findings of the Association for Computational Linguistics: ACL 2024, Bangkok, Thailand, August 11-16,

First Page

13423

Last Page

13439

Identifier

10.18653/v1/2024.findings-acl.796

City or Country

USA

Additional URL

https://doi.org/10.18653/v1/2024.findings-acl.796

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