---
language:
- en
pretty_name: Scientific Multitask Instructions
task_categories:
- text-generation
- question-answering
- summarization
tags:
- scientific
- instruction-tuning
- conversational
- supervised-fine-tuning
- grpo
size_categories:
- 1K<n<10K
---

# Scientific Multitask Instructions

A multi-task scientific instruction-following dataset created for
supervised fine-tuning and preference-optimization experiments.

## Dataset summary

The dataset contains 1,576 conversational scientific examples across
eight task types.

| Split | Examples |
|---|---:|
| Train | 1,260 |
| Validation | 158 |
| Test | 158 |
| Total | 1,576 |

## Task distribution

| Task | Examples |
|---|---:|
| Scientific question answering | 256 |
| Summarization | 220 |
| Concept explanation | 200 |
| Method comparison | 200 |
| Technical simplification | 200 |
| Bullet generation | 200 |
| Data analysis | 200 |
| Code generation | 100 |

## Fields

- `id`: unique example identifier
- `category`: scientific subject category
- `task`: instruction-task category
- `difficulty`: example difficulty
- `messages`: system, user, and assistant chat messages

## Usage

    from datasets import load_dataset

    dataset = load_dataset(
        "Miladsaeedi70/scientific-multitask-instructions",
        token=True,
    )

    print(dataset)
    print(dataset["train"][0])

The `token=True` argument is needed while the repository is private.

## Intended use

This dataset is intended for:

- Scientific instruction tuning
- Multi-task supervised fine-tuning
- LoRA and PEFT experiments
- Preference optimization
- GRPO reward-design research
- Scientific language-model evaluation

## Split methodology

Examples were divided into train, validation, and test sets using grouped
splitting to reduce prompt-template leakage between splits.

## Known limitations

- The dataset is relatively small.
- Some answers may contain scientific inaccuracies.
- Task categories are not equally represented.
- Lexical-overlap metrics do not fully measure factual correctness.
- Responses should be independently verified for high-stakes use.

## Publication status

The repository is initially private. Provenance, licensing, duplicate
checks, and sample quality should be reviewed before public release.