ChartCheck: Explainable Fact-Checking over Real-World Chart Images (2024.findings-acl)
Copied to clipboard
| Challenge: | Data visualizations are often used to summarize and communicate key information, but they can also be misused to spread misinformation and promote agendas. |
| Approach: | They propose a dataset for explainable fact-checking against real-world charts that uses vision-language and chart-to-table models to evaluate the validity of the dataset. |
| Outcome: | The proposed model is based on vision-language and chart-to-table models and proposes a baseline to the community. |
Similar Papers
Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing studies on detecting manipulated or fake images focus on identifying manipulated and false images. |
| Approach: | They propose a novel task, chart-based fact-checking, to validate textual, structural and visual information of charts to determine the veracity of textual claims. |
| Outcome: | The proposed model outperforms existing models in vision-language tests with 63.8% accuracy. |
Explainable Automated Fact-Checking: A Survey (2020.coling-main)
Copied to clipboard
| Challenge: | Steady progress has been made in fact-checking and its orthogonal tasks. |
| Approach: | They propose to use fact-checking explanations to explain predictions by comparing existing explanations against desirable properties to find out what makes for good explanations. |
| Outcome: | The proposed explanations are compared against desirable properties and show how they may lead to improvements in the research area. |
Fact-Checking Meets Fauxtography: Verifying Claims About Images (D19-1)
Copied to clipboard
| Challenge: | Recent explosion of false claims in social media has led to manual fact-checking initiatives . however, existing methods are inadequate to deal with the growing number of false content claims. |
| Approach: | They propose to model claims about images using a new dataset to examine the relationship between the image and the claim. |
| Outcome: | The proposed method improves on the baseline and will enable future research on fact-checking claims about images. |
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)
Copied to clipboard
Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li
| Challenge: | Existing fact-checking methods that use large language models often generate subtle factual errors. |
| Approach: | They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation. |
| Outcome: | GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call. |
A Survey on Automated Fact-Checking (2022.tacl-1)
Copied to clipboard
| Challenge: | Fact-checking is an essential task in journalism due to the speed with which information and misinformation can spread in the media ecosystem. |
| Approach: | They propose to use natural language processing to automate fact-checking by identifying common concepts and defining definitions. |
| Outcome: | The proposed method can predict the veracity of claims using natural language processing, machine learning, and databases. |
ClimateViz: A Benchmark for Statistical Reasoning and Fact Verification on Scientific Charts (2025.emnlp-main)
Copied to clipboard
| Challenge: | Scientific fact-checking has largely focused on textual and tabular sources, neglecting scientific charts. |
| Approach: | They propose a benchmark for scientific fact-checking grounded in scientific charts . climateViz comprises 49,862 claims paired with 2,896 visualizations . results show current models struggle to perform fact- checking when statistical reasoning is required . |
| Outcome: | The climateviz benchmark is the first large-scale benchmark for scientific fact-checking . it includes 49,862 claims paired with 2,896 visualizations labeled as support, refute, or not enough . |
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets that focus on complex reasoning questions do not address such questions as they are template-based and answers come from a fixed-vocabulary. |
| Approach: | They propose a large-scale benchmark that uses visual and logical reasoning to answer questions using a transformer-based model. |
| Outcome: | The proposed models achieve state-of-the-art on the previous datasets and on the current one, but also show that they have several challenges in answering complex reasoning questions. |
ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild (2025.coling-industry)
Copied to clipboard
| Challenge: | Existing methods for chart understanding and reasoning are weakly aligned and rely on underlying data tables. |
| Approach: | They propose a chart-based understanding and reasoning model that is trained on instruction-tuning data generated directly from chart images. |
| Outcome: | The proposed model achieves state-of-the-art results across 5 benchmarks spanning chart summarization, question answering, and fact-checking. |
Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning (2024.findings-emnlp)
Copied to clipboard
Mohammed Saidul Islam, Raian Rahman, Ahmed Masry, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, Enamul Hoque
| Challenge: | Recent studies have demonstrated that large vision language models (LVLMs) are not multi-modal and lack multi-tasking capabilities. |
| Approach: | They evaluate the performance of large vision language models (LVLMs) for chart understanding and reasoning tasks and compare them to open-source models. |
| Outcome: | The proposed models demonstrate impressive abilities in generating fluent texts covering high-level data insights, but they also encounter common problems like hallucinations, factual errors, and data bias. |
Is this chart lying to me? Automating the detection of misleading visualizations (2026.acl-long)
Copied to clipboard
| Challenge: | Prior work has shown that both humans and MLLMs are frequently deceived by misleading visualizations. |
| Approach: | They propose a benchmark of 2,604 real-world visualizations annotated with 12 types of misleaders. |
| Outcome: | The proposed framework can detect misleading visualizations and identify specific design rules they violate . the proposed framework is based on a synthetic dataset of 81,814 visualizations . |