Surprising Efficacy of Fine-Tuned Transformers for Fact-Checking over Larger Language Models

Author:

Setty Vinay1ORCID

Affiliation:

1. Factiverse AI, Stavanger, Norway

Funder

Norwegian Research Council

Publisher

ACM

Reference21 articles.

1. Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society

2. BRENDA: Browser Extension for Fake News Detection

3. Generating Literal and Implied Subquestions to Fact-check Complex Claims

4. Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, and William Yang Wang. 2021. FinQA: A Dataset of Numerical Reasoning over Financial Data. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP '21'), Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, 3697--3711.

5. I-Chun Chern Steffi Chern Shiqi Chen Weizhe Yuan Kehua Feng Chunting Zhou Junxian He Graham Neubig and Pengfei Liu. 2023. FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios. arxiv: 2307.13528 [cs.CL]

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