Update README.md

README.md

@@ -47,4 +47,48 @@ The original game itself is not well-posed, the solution is not unique, and list
 For each game the three distractor was chosen among all the possible italian words, the distractor was chosen to be aligned with 3 out of 5 hints and distant to the other ones (computing the cosine similarity in FastTest static embeddings).
 Moreover, the distractors was chosen to have lenght at most len(solution) + 1.
 
-With this setting, we created three different words that are not the possible solution of the game, making a task relativelly simple to be solved by humans, but not that much for Language Models.
+With this setting, we created three different words that are not the possible solution of the game, making a task relativelly simple to be solved by humans, but not that much for Language Models.
+
+## Example
+
+Here you can see the structure of the single sample in the present dataset.
+
+
+```json
+{
+  "w1": string, # text of the first hint
+  "w2": string, # text of the second hint
+  "w3": string, # text of the third hint
+  "w4": string, # text of the fourth hint
+  "w5": string, # text of the fifth hint
+  "choices": list, # list of possible words, with the correct one plus 3 distractors
+  "label": int, # index of the correct answer in the choices
+}
+```
+
+## Statistics 
+
+Training: -
+
+Test: -
+
+## 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: 
+```txt
+```
+
+Prompt:
+```txt
+```
+
+## Some Results
+
+| QUANDHO | ACCURACY |
+| :--------: | :----: |
+| Mistral-7B | 0 |
+| ZEFIRO | 0 |
+| Llama-3 | 0 |
+| ANITA | 0 |