metadata

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
        num_bytes: 906558
        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:
      - name: train
        num_bytes: 6705697
        num_examples: 664
      - name: test
        num_bytes: 2178281
        num_examples: 200
    download_size: 601925
    dataset_size: 8883978
  - 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
      - name: target
        dtype: int64
    splits:
      - name: train
        num_bytes: 14566247
        num_examples: 630
      - name: test
        num_bytes: 4882943
        num_examples: 200
    download_size: 1965578
    dataset_size: 19449190
  - config_name: precalculus
    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: 12491149
        num_examples: 816
      - name: test
        num_bytes: 3261896
        num_examples: 200
    download_size: 1513563
    dataset_size: 15753045
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-*
  - config_name: precalculus
    data_files:
      - split: train
        path: precalculus/train-*
      - split: test
        path: precalculus/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.

{
  "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, where with their first homework tried different interesting prompting strategies.

The data is freely available through this link.

License

The data come under the license Creative Commons Attribution Non Commercial Share Alike 4.0 International