Miladsaeedi70/scientific-multitask-instructions

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.

Task distribution

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.