Table-based fact verification aims to verify whether a statement sentence is trusted or fake.
Most existing methods rely on graph feature or data augmentation but fail to investigate …

Although remarkable progress on the neural table-to-text methods has been made, the
generalization issues hinder the applicability of these models due to the limited source …

Tables provide valuable knowledge that can be used to verify textual statements. While a
number of works have considered table-based fact verification, direct alignments of tabular …

Table-based fact verification task aims to verify whether the given statement is supported by
the given semi-structured table. Symbolic reasoning with logical operations plays a crucial …

Creating challenging tabular inference data is essential for learning complex reasoning.
Prior work has mostly relied on two data generation strategies. The first is human annotation …

Existing studies on table-based fact verification generally capture linguistic evidence from
claim-table subgraphs or logical evidence from program-table subgraphs independently …

Table-based fact verification aims to check whether a statement is entailed by the content of
relevant table. Existing works mainly either parse a statement with logical form or design a …

In recent years, attention-based models have excelled across various domains but remain
vulnerable to backdoor attacks, often from downloading or fine-tuning on poisoned datasets …

In the midst of widespread misinformation and disinformation through social media and the
proliferation of AI-generated texts, it has become increasingly difficult for people to validate …

Tables serve as a widely adopted data format, attracting considerable academic interest
concerning semantic understanding and logical inference of tables. In recent years, the …