Omkar2118/datasets huggingface dataset:
CHOCOLATE
Full name: Captions Have Often ChOsen Lies About The Evidence

**CHOCOLATE** is a benchmark for detecting and correcting factual inconsistency in generated chart captions. It consists of captions produced by six advanced models, which are categorized into three subsets:

- **LVLM**: GPT-4V, Bard (before Gemini)
- **LLM-based Pipeline**: DePlot + GPT-4
- **Fine-tuned Model**: ChartT5, MatCha, UniChart


The charts are from two datasets: VisText and the Pew split of Chart-to-Text. In total, **CHOCOLATE** consists of **1,187 examples**. Each instance in **CHOCOLATE** consists of a caption generated by one of the models and the annotations of the factual errors for each caption sentence.

### Paper Information

- Paper: [https://arxiv.org/abs/2312.10160](https://arxiv.org/abs/2312.10160)
- Code: [https://github.com/khuangaf/CHOCOLATE/](https://github.com/khuangaf/CHOCOLATE/)
- Project: [https://khuangaf.github.io/CHOCOLATE](https://khuangaf.github.io/CHOCOLATE)


### Citation

If you use the **CHOCOLATE** dataset in your work, please kindly cite the paper using this BibTeX:

```
@misc{huang-etal-2023-do,
    title = "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning",
    author = "Huang, Kung-Hsiang  and
      Zhou, Mingyang and
      Chan, Hou Pong  and
      Fung, Yi R. and
      Wang, Zhenhailong and
      Zhang, Lingyu and
      Chang, Shih-Fu and
      Ji, Heng",
    year={2023},
    eprint={2312.10160},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}    
```

Introducing paper: Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning