Update README.md

README.md

@@ -83,3 +83,93 @@ configs:
   - split: test
     path: physics/test-*
 ---
+
+# Prerequisite RElation LEARNing (PRELEARN)
+
+Original Paper: https://ceur-ws.org/Vol-2765/paper164.pdf
+
+This dataset contains a collection of binary-labelled concept pairs (A,B) extracted from textbooks on four domains: **data mining**, **geometry**, **physics** and **precalculus**.
+Then, domain experts were asked to manually annotate if pairs of concepts showed a prerequisite relation or not, therefore the dataset consists of both positive and negative concept pairs.
+
+We obtained the data from the original repository, making only one modification: undersampling the training data. To evaluate generative models in in-context learning, it's essential to have a balanced distribution for sampling examples in a few-shot setting. The undersampling process was carried out randomly, and separately for each domain.
+
+## Example
+
+Here you can see the structure of the single sample in the present dataset.
+
+```json
+{
+  "concept_A": string, # text of the concept A
+  "wikipedia_passage_concept_A": string, # paragraph of wikipedia corresponding to concept A
+  "concept_B": string, # text of the concept B
+  "wikipedia_passage_concept_B": string, # paragraph of wikipedia corresponding to concept B
+  "target": int, # 0: B non è preconcetto di A, 1: B è preconcetto di A
+}
+```
+
+## Statitics
+
+| PRELEARN Data Mining | 0 | 1 |
+| :--------: | :----: | :----: |
+| Training | 109 | 109 |
+| Test | 50 | 49 |
+
+| PRELEARN Physics | 0 | 1 |
+| :--------: | :----: | :----: |
+| Training | 315 | 315 |
+| Test | 100 | 100 |
+
+| PRELEARN Geometry | 0 | 1 |
+| :--------: | :----: | :----: |
+| Training | 332 | 332 |
+| Test | 100 | 100 |
+
+| PRELEARN Precalculus | 0 | 1 |
+| :--------: | :----: | :----: |
+| Training | 408 | 408 |
+| Test | 100 | 100 |
+
+## 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: "Dati due concetti, indica se primo concetto è un prerequisito o meno per il secondo.\nUn concetto A è prerequisito per un concetto B, se per comprendere B devo prima aver compreso A.\nI seguenti concetti appartengono al dominio: {{domain}}.\n\n"
+
+### Cloze Style:
+
+Label (**B non è prerequisito di A**): "{{concept_B}} non è un prerequisito per {{concept_A}}"
+
+Label (**B è prerequisito di A**): "{{concept_B}} è un prerequisito per {{concept_A}}"
+
+### MCQA Style:
+
+```
+Domanda: il concetto {{concept_B}} è un prerequisito per la comprensione del concetto {{concept_A}}? Rispondi sì o no:
+```
+
+## Some Results
+
+The following results are given by the Cloze-style prompting over some english and italian-adapted LLMs.
+
+| PRELEARN (AVG) | ACCURACY (15-shots) |
+| :-----: | :--: |
+| Gemma-2B | 60.12 |
+| QWEN2-1.5B | 57.00 |
+| Mistral-7B | 64.50 |
+| ZEFIRO | 64.76 |
+| Llama-3-8B | 60.63 |
+| Llama-3-8B-IT | 63.76 |
+| ANITA | 63.77 |
+
+## Aknwoledge
+
+We want to thanks this resource's authors for publicly releasing such an interesting dataset.
+
+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 is freely available through this [link](https://live.european-language-grid.eu/catalogue/corpus/8084).
+
+## License
+
+The data come under the license [Creative Commons Attribution Non Commercial Share Alike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)