---
dataset_info:
  features:
  - name: question
    dtype: string
  - name: choices
    sequence: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 619513
    num_examples: 384
  - name: test
    num_bytes: 2301030
    num_examples: 1416
  download_size: 1491635
  dataset_size: 2920543
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# QUANDHO: QUestion ANswering Data for italian HistOry

Original Paper: https://aclanthology.org/L16-1069.pdf

QUANDHO (QUestion ANswering Data for italian HistOry) is an Italian question answering dataset created to cover the history of Italy in the first half of the XX century.

Starting from QUANDHO we defined a Multi-choice QA dataset, with a correct answer and four different distractors.

## Data and Distractors Generation

We relied on the original data, to create this dataset. For each question-answer correct pair, we defined a dataset sample. For each sample, we gather four different distractors from incorrect question-answer pairs, where the question is the one of the chosen sample.

## Example

Here you can see the structure of the single sample in the present dataset.


```json
{
  "text": string, # text of the question
  "choices": list, # list of possible answers, with the correct one plus 3 distractors
  "label": int, # index of the correct anser in the choices
}
```

## Statistics 

Training: 384

Test: 1416

## Proposed Prompts

Here we will describe the prompt given to the model over which we will compute the perplexity score, as model's answer we will chose the prompt with lower perplexity.
Moreover, for each subtask, we define a description that is prepended to the prompts, needed by the model to understand the task.

Description of the task: 
```txt
Ti saranno poste domande di storia italiana.\nIdentifica quali paragrafi contengono la risposta alle domande date.\n\n
```

Prompt:
```txt
Data la domanda: \"{{question}}\"\nQuale tra i seguenti paragrafi risponde alla domanda?\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nRisposta:
```

## Results

| QUANDHO | ACCURACY (2-shots) |
| :-----: | :--: |
| Gemma-2B | 43.99 |
| QWEN2-1.5B | 56.43 |
| Mistral-7B | 72.66 |
| ZEFIRO | 70.12|
| Llama-3-8B | 70.26 |
| Llama-3-8B-IT | 81.07 |
| ANITA | 74.29 |


## Acknowledgment

The original data can be downloaded from the following [link](https://dh.fbk.eu/2016/03/quandho-question-answering-data-for-italian-history/)

We want to thank the dataset's creators, that release such interesting resource publicly.

## License

The original dataset is licensed under [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)