metadata

task_categories:
  - text-generation
language:
  - it
size_categories:
  - 1K<n<10K
configs:
  - config_name: italian
    data_files:
      - split: test
        path: medical_school_questions.jsonl
tags:
  - culture
  - knowledge
  - chemistry
  - biology
  - math
  - reasoning
pretty_name: Medschool Test

Medschool-Test, or "Test di Medicina"

Is your LLM able to pass a National Entrance Exam for the Italian Medical School?

This a Huggingface dataset designed for evaluating Language Model (LLM) on a broad range of questions from the national entrance exams for the Italian medical school. The dataset includes multiple-choice questions from various subjects such as biology, chemistry, physics, mathematics, world knowledge, and more. Each question is accompanied by five answer choices, with one correct answer.

Features

Multiple topics: Questions cover a wide range of subjects, including biology, chemistry, physics, mathematics, world knowledge (with a focus on Italian culture), and more.
"Multiple-choice" and "Cloze-style" formats: Each question has five answer choices, with one correct answer. This dataset is designed to evaluate LLMs on both multiple-choice and cloze-style questions. More specifically, each question is presented in the following formats:
- Multiple-choice: The question is followed by five answer choices, with one correct answer. The evaluation metrics is based on the model's ability to select the correct answer when presented with the question and answer choices.
- Cloze-style: The question is NOT followed by answer choices. The evaluation metric is based on the model's ability to generate the correct answer when presented with the question only.
Large-scale: The dataset contains over 3K high-quality questions, making it suitable for the evaluation of LLMs.
Italian (and English coming soon): The dataset is currently available in Italian, with an English version coming soon.

Evaluation

The dataset is designed to evaluate LLMs on a wide range of questions from medical school entrance exams. The evaluation metrics are based on the model's ability to select the correct answer when presented with the question and answer choices (multiple-choice format) or generate the correct answer when presented with the question only (cloze-style format).

Scoring

For each question, the model obtains a score ranging from -0.4 to 1.5, based on the following criteria:

Correct Answer: If the model selects the correct answer, it receives a score of 1.5.
Incorrect Answer: If the model selects an incorrect answer, it receives a score of -0.4.
No Answer: If the model does not select any answer, it receives a score of 0.0.

The final score is calculated as the weighted average of the scores obtained on all questions, where the weight of each question depends on the topic of the question, as follows:

Subject	Score	Weight
Biology	23/60	0.3833
Chemistry	15/60	0.25
Math & Physics	13/60	0.2167
World Knowledge	4/60	0.0667
Logic & Reasoning	5/60	0.0833

This is the same scoring system used for the official Italian medical school entrance exams.

Evaluation Script

We will release the evaluation script soon. Stay tuned!

The evaluation is based on the lm-evaluation-harness library, which provides a simple and flexible way to evaluate LLMs on a wide range of tasks and datasets. The tasks are defined in tasks/medschool-entrance-exams.

To run the evaluation, you can use the following command:

MODEL_ARGS="pretrained=meta-llama/Meta-Llama-3.1-8B-Instruct,dtype=bfloat16"

lm_eval \
  --model hf \
  --model_args $MODEL_ARGS \
  --tasks medschool_entrance_exams_it_mc,medschool_entrance_exams_it_cloze \
  --batch_size auto \
  --log_samples \
  --output_path outputs/ \
  --include tasks/medschool-entrance-exams/

This command evaluates the model meta-llama/Meta-Llama-3.1-8B-Instruct on the Italian version of the dataset in both multiple-choice and cloze-style formats. The evaluation results are saved in the outputs/ directory.

Please, refer to the lm-evaluation-harness repository for more details on how to use the library.

Data

Source

The dataset is collected from the official Italian website of the Ministry of Education, University and Research (MIUR), which hosts a large collection of past entrance exams for medical school in Italy. The dataset includes questions from various subjects, such as biology, chemistry, physics, mathematics, world knowledge, and more. You can find the original dataset here.

Composition

The dataset contains over 3K questions, making it suitable for training and evaluating LLMs. The following table shows the number of questions in the dataset for each subject:

Subject	Number of questions
Biology	1,180
Chemistry	1,009
Math & Physics	655
World knowledge	245
Logic & Reasoning	212
Total	3,301

Size comparison with other benchmarks

The dataset contains over 3K questions, making it suitable for training and evaluating LLMs. The following table shows the size of the dataset compared to other benchmarks:

Dataset	Number of questions
Medical School Entrance Exams	~3,300
ARC-Challenge (test)	~1,170
ARC-Easy (test)	~2,380
BoolQ (validation)	~3,270
CommonSenseQA (validation)	~1,220
GSM8K (test)	~1,320
MMLU (test)	~14,000
PIQA (validation)	~1,850
SciQ (test)	~1,000
TruthfulQA (test)	~820
WinoGrande (test)	~1,767

Format

The dataset is provided in JSONL format, with each line representing a single question in the following format:

{
  "id": 1691,
  "topic": "biologia",
  "text": "Come sono definite le cellule staminali che sono in grado di differenziarsi in tutti i tipi di cellule presenti nel corpo umano, ma non possono dare origine ad un organismo completo?",
  "answers": [
    "Cellule Staminali Multipotenti",
    "Cellule Staminali Pluripotenti",
    "Cellule Staminali Totipotenti",
    "Cellule Staminali Unipotenti",
    "Cellule Staminali Oligopotenti"
  ],
  "label": 1
}

where:

id (int): The unique identifier of the question.
topic (str): The subject of the question.
text (str): The text of the question.
answers (list): A list of five answer choices, with one correct answer.
label (int): The index of the correct answer in the answers list (0-indexed).

Examples

Biology

{
  "id": 1691,
  "topic": "biologia",
  "text": "Come sono definite le cellule staminali che sono in grado di differenziarsi in tutti i tipi di cellule presenti nel corpo umano, ma non possono dare origine ad un organismo completo?",
  "answers": [
    "Cellule Staminali Multipotenti",
    "Cellule Staminali Pluripotenti",
    "Cellule Staminali Totipotenti",
    "Cellule Staminali Unipotenti",
    "Cellule Staminali Oligopotenti"
  ],
  "label": 1
}

Chemistry

{
  "id": 19,
  "topic": "chimica",
  "text": "Rispetto alla classificazione che si trova nella tavola periodica il fluoro fa parte del: ",
  "answers": [
    "gruppo dei gas nobili",
    "gruppo degli alogeni",
    "gruppo dei lantanidi",
    "secondo gruppo",
    "quarto periodo"
  ],
  "label": 1
}

Math & Physics

{
  "id": 1203,
  "topic": "fisica-matematica",
  "text": "Quali sono le coordinate del centro della circonferenza di equazione x^2 + y^2 + 2x – 6y + 5 = 0?",
  "answers": [
    "(2 ; –6)",
    "(–2 ; 6)",
    "(1 ; 3)",
    "(–1 ; 3)",
    "(2 ; 3)"
  ],
  "label": 3
}

World Knowledge

{
  "id": 11,
  "topic": "competenze-conoscenze",
  "text": "Quale dei seguenti è il primo romanzo di Italo Calvino, pubblicato nel 1947?",
  "answers": [
    "Se una notte d'inverno un viaggiatore",
    "Il barone rampante",
    "Palomar",
    "Le cosmicomiche",
    "Il sentiero dei nidi di ragno"
  ],
  "label": 4
}

Logic & Reasoning

{
  "id": 2539,
  "topic": "logica",
  "text": "Tutti i pasticcieri praticano il kendo; Gianluca pratica il kendo. Quale delle seguenti affermazioni aggiuntive consentirebbe di dedurre con certezza che Gianluca è un pasticciere?",
  "answers": [
    "Tra le persone che praticano kendo vi sono dei pasticcieri",
    "Alcune persone che praticano kendo si chiamano Gianluca",
    "Alcune persone che praticano kendo sono pasticcieri",
    "Non è certo che ogni persona che pratica kendo sia anche un pasticciere",
    "Ogni persona che pratica kendo è anche un pasticciere"
  ],
  "label": 4
}

Contributing

Contributions to this dataset are welcome! If you have additional tasks or domains that you would like to include, please submit a pull request.

License

This dataset is released under the Apache 2.0 License. Feel free to use it for research, commercial, or personal purposes.