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--- |
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dataset_info: |
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features: |
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- name: lemma |
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dtype: string |
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- name: sentence1 |
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dtype: string |
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- name: sentence2 |
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dtype: string |
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- name: start1 |
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dtype: int64 |
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- name: end1 |
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dtype: int64 |
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- name: start2 |
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dtype: int64 |
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- name: end2 |
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dtype: int64 |
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- name: label |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 1128581 |
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num_examples: 2805 |
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- name: validation |
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num_bytes: 198885 |
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num_examples: 500 |
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- name: test |
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num_bytes: 199696 |
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num_examples: 500 |
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download_size: 1012507 |
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dataset_size: 1527162 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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--- |
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# Word in Context (WIC) |
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Original Paper: https://wic-ita.github.io/ |
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This dataset comes from EVALITA-2023. |
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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. |
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We repropose this task to test generative LLMs defining a specific prompting strategy comparing the perplexities of possible continuations to understand the models' capabilities. |
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## Example |
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Here you can see the structure of the single sample in the present dataset. |
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```json |
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{ |
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"sentence_1": string, # text of the sentence 1 |
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"sentence_2": string, # text of the sentence 2 |
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"lemma": string, # text of the word present in both sentences |
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"label": int, # 0: Different Mearning, 1: Same Meaning, |
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} |
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``` |
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## Statistics |
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| WIC | 0 | 1 | |
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| :--------: | :----: | :----: | |
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| Training | 806 | 1999 | |
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| Validation | 250 | 250 | |
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| Test | 250 | 250 | |
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## Proposed Prompts |
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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. |
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Moreover, for each subtask, we define a description that is prepended to the prompts, needed by the model to understand the task. |
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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" |
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### Cloze Style: |
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Label (**Different Meaning**): "Frase 1: {{sentence1}}\nFrase 2: {{sentence2}}\nLa parola '{{lemma}}' nelle due frasi precedenti ha un significato differente" |
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Label (**Same Meaning**): "Frase 1: {{sentence1}}\nFrase 2: {{sentence2}}\nLa parola '{{lemma}}' nelle due frasi precedenti ha lo stesso significato" |
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### MCQA Style: |
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```txt |
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Frase 1: {{sentence1}}\nFrase 2: {{sentence2}}\nDomanda:La parola '{{lemma}}' nelle due frasi precedenti ha lo stesso signicato? Rispondi sì o no: |
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``` |
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## Some Results |
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The following results are given by the Cloze-style prompting over some english and italian-adapted LLMs. |
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| WIC | ACCURACY (5-shots) | |
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| :-----: | :--: | |
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| Gemma-2B | 48.2 | |
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| QWEN2-1.5B | 50.4 | |
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| Mistral-7B | 53.4 | |
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| ZEFIRO | 54.6 | |
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| Llama-3-8B | 54.6 | |
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| Llama-3-8B-IT | 62.8 | |
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| ANITA | 69.2 | |
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## Acknowledge |
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We want to thanks the authors of this resource to publicly release such interesting benchmark. |
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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. |
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The data can be freely downloaded form this [link](https://github.com/wic-ita/data). |
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## License |
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Original data license not found. |
