---
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 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 tra le due frasi"

Label (**Same Meaning**): "Frase 1: {{sentence1}}\nFrase 2: {{sentence2}}\nLa parola '{{lemma}}' nelle due frasi precedenti ha lo stesso significato in entrambe le frasi"

### MCQA Style:

```txt
Frase 1: {{sentence1}}\nFrase 2: {{sentence2}}\nDomanda: La parola \"{{lemma}}\" ha lo stesso signicato nelle due frasi precedenti? Rispondi sì o no:
```

## 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 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](https://naviglinlp.blogspot.com/), whose first homework explored various interesting prompting strategies.

The original dataset is freely available for download [link](https://github.com/wic-ita/data).

## License

Original data license not found.