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---
dataset_info:
- config_name: data_mining
features:
- name: wikipedia_passage_concept_A
dtype: string
- name: concept_A
dtype: string
- name: wikipedia_passage_concept_B
dtype: string
- name: concept_B
dtype: string
- name: target
dtype: int64
splits:
- name: train
num_bytes: 2356292
num_examples: 218
- name: test
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num_examples: 99
download_size: 564203
dataset_size: 3262850
- config_name: geometry
features:
- name: wikipedia_passage_concept_A
dtype: string
- name: concept_A
dtype: string
- name: wikipedia_passage_concept_B
dtype: string
- name: concept_B
dtype: string
- name: target
dtype: int64
splits:
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download_size: 601925
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- config_name: physics
features:
- name: wikipedia_passage_concept_A
dtype: string
- name: concept_A
dtype: string
- name: wikipedia_passage_concept_B
dtype: string
- name: concept_B
dtype: string
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dtype: int64
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- name: train
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configs:
- config_name: data_mining
data_files:
- split: train
path: data_mining/train-*
- split: test
path: data_mining/test-*
- config_name: geometry
data_files:
- split: train
path: geometry/train-*
- split: test
path: geometry/test-*
- config_name: physics
data_files:
- split: train
path: physics/train-*
- 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/) |