Factual Inconsistency Detection in Chart Captioning
4 papers with code • 4 benchmarks • 1 datasets
Detect factual inconsistency between charts and captions.
Benchmarks
These leaderboards are used to track progress in Factual Inconsistency Detection in Chart Captioning
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Factual Inconsistency Detection in Chart Captioning models and implementations
CHOCOLATE
Most implemented papers
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Improved Baselines with Visual Instruction Tuning
Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning.
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation mechanism, and demonstrating an effective approach to ensuring the factuality of generated chart captions.
DePlot: One-shot visual language reasoning by plot-to-table translation
Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24. 0% improvement over finetuned SOTA on human-written queries from the task of chart QA.
