dataset_info:
features:
- name: text
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 16078856
num_examples: 16000
- name: test
num_bytes: 1591516
num_examples: 1600
download_size: 10736830
dataset_size: 17670372
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
Assessing DIScourse COherence in Italian TEXts (DISCOTEX)
Original Paper: https://sites.google.com/view/discotex/
Task presented at EVALITA-2023
The original task is about modelling discourse coherence for Italian texts.
We focalized only on the first sub-task: Last Sentence Classification: given a short paragraph, and an individual sentence (target), the model will be asked to classify whether the target follows or not the paragraph.
To assess the capability of a Language Model to solve such kind of task we reframed the task as Multi-Choice QA.
The question will ask to the model given a short paragraph which target sentence is the correct between a list of four, the answers will be the starting letters of the relative target, and a fifth option that indicate that no one target is the correct continuation.
Distractors Generation
For each sample, if the sample has 1 as label, we set the relative target as gold answer and three other random targets (from other samples) as distractors. On the other way around, if the sample has 0 as label, we set the relative target and other three random targets (from other samples) as distractors, as the gold answer will be chosen the sentence: "nessuna delle precedenti".
Example
Here you can see the structure of the single sample in the present dataset.
{
"text": string, # text of the short paragraph
"choices": list, # list of possible answers, with the correct one plus 4 distractors
"label": int, # index of the correct anser in the choices
}
Statistics
Training: 16000
Test: 1600
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:
Ti verranno poste delle domande nelle quali è presente un paragrafo, e come possibili risposte varie frasi che possono essere o meno la continuazione del paragrafo.\nIndica la frase che rappresenta la continuazione più probabile del paragrafo, oppure \"nessuna delle precedenti\" se nessuna delle continuazioni è corretta.\n\n
Prompt:
Paragrafo: \"{{text}}\"\nDomanda: Quali delle seguenti frasi è la continuazione più probabile del precedente paragrafo?\nA. \"{{choices[0]}}\"\nB. \"{{choices[1]}}\"\nC. \"{{choices[2]}}\"\nD. \"{{choices[3]}}\"\nE. {{choices[4]}}\nRisposta:
Results
| DISCOTEX | ACCURACY (2-shots) |
|---|---|
| Gemma-2B | 19.18 |
| QWEN2-1.5B | 35.18 |
| Mistral-7B | 56.43 |
| ZEFIRO | 53.68 |
| Llama-3-8B | 58.56 |
| Llama-3-8B-IT | 66.12 |
| ANITA | 66.37 |
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, whose first homework explored various interesting prompting strategies.
The original dataset is freely available for download link.
License
The original data come under license CC-BY-NA 4.0