dataset_info:
features:
- name: lemma
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: start1
dtype: int64
- name: end1
dtype: int64
- name: start2
dtype: int64
- name: end2
dtype: int64
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1128581
num_examples: 2805
- name: validation
num_bytes: 198885
num_examples: 500
- name: test
num_bytes: 199696
num_examples: 500
download_size: 1012507
dataset_size: 1527162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
Word in Context (WIC)
Original Paper: https://wic-ita.github.io/
This dataset comes from EVALITA-2023.
Word in Context task consists of establishing if a word w occurring in two different sentences s1 and s2 has the same meaning or not.
We repropose this task to test generative LLMs defining a specific prompting strategy comparing the perplexities of possible continuations to understand the models' capabilities.
Example
Here you can see the structure of the single sample in the present dataset.
{
"sentence_1": string, # text of the sentence 1
"sentence_2": string, # text of the sentence 2
"lemma": string, # text of the word present in both sentences
"label": int, # 0: Different Mearning, 1: Same Meaning,
}
Statistics
| WIC | 0 | 1 |
|---|---|---|
| Training | 806 | 1999 |
| Validation | 250 | 250 |
| Test | 250 | 250 |
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: "Date due frasi, che contengono un lemma in comune, indica se tale lemma ha o meno lo stesso significato in entrambe le frasi.\n\n"
Cloze Style:
Label (Different Meaning): "Frase 1: {{sentence1}}\nFrase 2: {{sentence2}}\nLa parola '{{lemma}}' nelle due frasi precedenti ha un significato differente"
Label (Same Meaning): "Frase 1: {{sentence1}}\nFrase 2: {{sentence2}}\nLa parola '{{lemma}}' nelle due frasi precedenti ha lo stesso significato"
MCQA Style:
Frase 1: {{sentence1}}\nFrase 2: {{sentence2}}\nDomanda:La parola '{{lemma}}' nelle due frasi precedenti ha lo stesso signicato? Rispondi sì o no:
Some Results
The following results are given by the Cloze-style prompting over some english and italian-adapted LLMs.
| WIC | ACCURACY (5-shots) |
|---|---|
| Gemma-2B | 48.2 |
| QWEN2-1.5B | 50.4 |
| Mistral-7B | 53.4 |
| ZEFIRO | 54.6 |
| Llama-3-8B | 54.6 |
| Llama-3-8B-IT | 62.8 |
| ANITA | 69.2 |
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 data can be freely downloaded form this link.
License
Original data license not found.