Table-based Fact Verification
14 papers with code • 1 benchmarks • 2 datasets
Verifying facts given semi-structured data.
TabFact
InfoTabS
Latest papers with no code
Are Large Language Models Table-based Fact-Checkers?
Finally, we analyze some possible directions to promote the accuracy of TFV via LLMs, which is beneficial to further research of table reasoning.
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts.
ConTFV: A Contrastive Learning Framework for Table-based Fact Verification
Table-based fact verification is a binary classification task where the challenging part lies in the table's structural parsing and symbolic reasoning.
Table-based Fact Verification with Self-adaptive Mixture of Experts
The table-based fact verification task has recently gained widespread attention and yet remains to be a very challenging problem.
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification
Starting from the Table Parsing (TAPAS) model developed for question answering (Herzig et al., 2020), we find that modeling table structure improves a language model pre-trained on unstructured text.
Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification
Table-based fact verification is expected to perform both linguistic reasoning and symbolic reasoning.
