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--- |
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dataset_info: |
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features: |
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- name: question |
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dtype: string |
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- name: choices |
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sequence: string |
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- name: label |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 619513 |
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num_examples: 384 |
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- name: test |
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num_bytes: 2301030 |
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num_examples: 1416 |
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download_size: 1491635 |
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dataset_size: 2920543 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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--- |
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# QUANDHO: QUestion ANswering Data for italian HistOry |
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Original Paper: https://aclanthology.org/L16-1069.pdf |
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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. |
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Starting from QUANDHO we defined a Multi-choice QA dataset, with a correct answer and four different distractors. |
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## Data and Distractors Generation |
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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. |
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## Example |
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Here you can see the structure of the single sample in the present dataset. |
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```json |
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{ |
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"text": string, # text of the question |
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"choices": list, # list of possible answers, with the correct one plus 3 distractors |
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"label": int, # index of the correct anser in the choices |
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} |
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``` |
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## Statistics |
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Training: 384 |
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Test: 1416 |
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## Proposed Prompts |
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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. |
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Moreover, for each subtask, we define a description that is prepended to the prompts, needed by the model to understand the task. |
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Description of the task: |
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```txt |
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Ti saranno poste domande di storia italiana.\nIdentifica quali paragrafi contengono la risposta alle domande date.\n\n |
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``` |
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Prompt: |
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```txt |
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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: |
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``` |
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## Results |
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| QUANDHO | ACCURACY (2-shots) | |
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| :-----: | :--: | |
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| Gemma-2B | 43.99 | |
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| QWEN2-1.5B | 56.43 | |
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| Mistral-7B | 72.66 | |
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| ZEFIRO | 70.12| |
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| Llama-3-8B | 70.26 | |
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| Llama-3-8B-IT | 81.07 | |
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| ANITA | 74.29 | |
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## Acknowledgment |
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The original data can be downloaded from the following [link](https://dh.fbk.eu/2016/03/quandho-question-answering-data-for-italian-history/) |
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We want to thank the dataset's creators, that release such interesting resource publicly. |
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## License |
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The original dataset is licensed under [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/) |
