README.md

---
dataset_info:
- config_name: Behaviour
  features:
  - name: text
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  - name: label
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  - name: test
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configs:
- config_name: Behaviour
  data_files:
  - split: train
    path: Behaviour/train-*
  - split: test
    path: Behaviour/test-*
- config_name: Synth
  data_files:
  - split: train
    path: Synth/train-*
  - split: test
    path: Synth/test-*
---

# Automatic Misogyny Identification (AMI)

Original Paper: https://amievalita2020.github.io

Task presented at EVALITA-2020

This task consists of tweet classification, specifically, categorization of the level of misogyny in a given text.

We taken both subtasks, *raw_dataset* uploaded as *Behaviour* (3 class classification) and *synthetic* uploaded as *Synth* (2 class classification).

## Example

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

### Behaviour

```json
{
  "text": string, # text of the tweet
  "label": int, # 0: Non Misogino, 1: Misogino, 2: Misogino Aggressivo
}
```

### Synth

```json
{
  "text": string, # text of the tweet
  "label": int, # 0: Non Misogino, 1: Misogino
}
```

## Statitics

| AMI Behaviour | Non Misogino | Misogino | Misogino Aggressivo |
| :--------: | :----: | :----: | :----: |
| Training | 2663 | 554 | 1783 |
| Test | 500 | 324 | 176 |

| AMI Synth | Non Misogino | Misogino |
| :--------: | :----: | :----: |
| Training | 3670 | 3344 |
| Test | 1454 | 1454 |


## 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.

### Behaviour

Description of the task: "Indica il livello di misoginia presente nei seguenti tweet.\n\n"


#### Cloze Style:

Label (**Non Misogino**): "Tweet: '{{text}}'.\nIl tweet non presenta caratteristiche misogine."

Label (**Misogino**): "Tweet: '{{text}}'.\nIl tweet presenta caratteristiche misogine."

Label (**Misogino Aggressivo**): "Tweet: '{{text}}'.\nIl tweet presenta caratteristiche misogine aggressive."

#### MCQA Style:

```txt
Tweet: '{{text}}'.\nDomanda: che livello di misoginia è presente nel tweet?\nA. Nessuno\nB. Misogino\nC. Misogino Aggressivo\nRisposta:
```

### Synth
Description of the task: "Indica se i seguenti tweet presentano caratteristiche misogine.\n\n"

#### Cloze Style:

Label (**Non Misogino**): "Tweet: '{{text}}'.\nIl tweet non presenta caratteristiche misogine."

Label (**Misogino**): "Tweet: '{{text}}'.\nIl tweet presenta caratteristiche misogine."


#### MCQA Style:

```txt
Tweet: '{{text}}'.\nDomanda: Il tweet contiene elementi misogini? Rispondi sì o no:
```

## Results

The following results are given by the Cloze-style prompting over some english and italian-adapted LLMs.

| AMI Synth | ACCURACY (5-shots) |
| :-----: | :--: |
| Gemma-2B | 53.78 |
| QWEN2-1.5B | 60.72 |
| Mistral-7B | 71.59 |
| ZEFIRO | 74.69 |
| Llama-3-8B | 74.55 |
| Llama-3-8B-IT | 78.47 |
| ANITA | 82.66 |

## Acknowledge

We would like to thank the authors of this resource for publicly releasing such an intriguing benchmark.

Additionally, we extend our gratitude to the students of the [MNLP-2024 course](https://naviglinlp.blogspot.com/), whose first homework explored various interesting prompting strategies.

The original dataset is freely available for download [link](https://live.european-language-grid.eu/catalogue/corpus/7005/download/).

## License

The data come under license [Creative Commons Attribution Non Commercial Share Alike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/).