Robust and resource-efficient table-based fact verification through multi-aspect adversarial contrastive learning-Reference-Cited by-同舟云学术

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Robust and resource-efficient table-based fact verification through multi-aspect adversarial contrastive learning

Published:2024-11 Issue:6 Volume:61 Page:103853
ISSN:0306-4573
Container-title:Information Processing & Management
language:en
Short-container-title:Information Processing & Management

Author:

Liu Ruiheng^ORCID,Zhang Yu,Yang Bailong,Shi Qi,Tian Luogeng

Funder

Harbin Institute of Technology

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference80 articles.

1. FinQA: A dataset of numerical reasoning over financial data;Chen,2021

2. A simple framework for contrastive learning of visual representations;Chen,2020

3. Evaluating large language models trained on code;Chen,2021

4. TabFact: A large-scale dataset for table-based fact verification;Chen,2020

5. Binding language models in symbolic languages;Cheng,2023

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