---
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.

```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

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](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.