metadata

dataset_info:
  - config_name: Behaviour
    features:
      - name: text
        dtype: string
      - name: choices
        sequence: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 883966
        num_examples: 5000
      - name: test
        num_bytes: 183067
        num_examples: 1000
    download_size: 458408
    dataset_size: 1067033
  - config_name: Synth
    features:
      - name: text
        dtype: string
      - name: choices
        sequence: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 800941
        num_examples: 7014
      - name: test
        num_bytes: 248483
        num_examples: 2908
    download_size: 502169
    dataset_size: 1049424
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

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

Synth

{
  "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 tweets."

Cloze Style:

Label (Non Misogino): "Tweet: '{{text}}'.\nIl tweet non presenta alcun elemento misogino"

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

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

MCQA Style:

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 tweets presentano caratteristiche o elementi misogini."

Cloze Style:

Label (Non Misogino): "Tweet: '{{text}}'.\nIl tweet non presenta alcun elemento misogino"

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

MCQA Style:

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 want to thanks the authors of this resource to publicly release such interesting benchmark.

Further, We want to thanks the student of MNLP-2024 course, where with their first homework tried different interesting prompting strategies.

The original data can be freely downloaded form this link.

License

The data come under license Creative Commons Attribution Non Commercial Share Alike 4.0 International.