Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

Fact-checking is an essential task in NLP that is commonly utilized for validating the factual
accuracy of claims. Prior work has mainly focused on fine-tuning pre-trained languages …

Misinformation is often conveyed in multiple modalities, eg a miscaptioned image.
Multimodal misinformation is perceived as more credible by humans, and spreads faster …

Evidence data for automated fact-checking (AFC) can be in multiple modalities such as text,
tables, images, audio, or video. While there is increasing interest in using images for AFC …

Numbers are crucial for various real-world domains such as finance, economics, and
science. Thus, understanding and reasoning with numbers are essential skills for language …

Tables contrast with unstructured text data by its structure to organize the information. In this
paper, we investigate the efficiency of various LLMs in interpreting tabular data through …

Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases
arising from crowd-sourced claims and an over-reliance on text-based evidence. We present …

Recent advancements in Large Language Models (LLMs) have led to significant
breakthroughs in various natural language processing tasks. However, generating factually …

Data visualizations are common in the real-world. We often use them in data sources such
as scientific documents, news articles, textbooks, and social media to summarize key …

Whilst fact verification has attracted substantial interest in the natural language processing
community, verifying misinforming statements against data visualizations such as charts has …