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---
dataset_info:
features:
- name: id
dtype: int64
- 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: choices
sequence: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 1235171
num_examples: 2805
- name: validation
num_bytes: 217885
num_examples: 500
- name: test
num_bytes: 218696
num_examples: 500
download_size: 1037141
dataset_size: 1671752
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.
```json
{
"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:
```txt
Frase 1: {{sentence1}}\nFrase 2: {{sentence2}}\nDomanda:La parola '{{lemma}}' nelle due frasi precedenti ha lo stesso signicato (Rispondi sì o no)?
```
## Some Results
| 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](https://naviglinlp.blogspot.com/), where with their first homework tried different interesting prompting strategies.
The data can be freely downloaded form this [link](https://github.com/wic-ita/data).
## License
Original data license not found. |