Muk-Jji-Bba Dataset (SquidGame Series)
Note: Please do not use this dataset for training purposes.
Overview
The "Muk-Jji-Bba" dataset is the first in the SquidGame series, designed to evaluate whether models can understand human behavior. This dataset specifically focuses on the game of Muk-Jji-Bba, a variation of Rock-Paper-Scissors widely played in Korea.
How Muk-Jji-Bba Works:
- The attacker tries to match their gesture with the defender’s to win the game.
- If both players show the same gesture in the next round, the attacker wins.
- If the attacker’s next gesture loses to the defender’s, the roles switch, and the defender becomes the new attacker.
- If the attacker’s next gesture beats the defender’s, the attacker keeps their role and the game continues.
The dataset includes 4 rounds per situation. If there is no winner in the final round, the result is a "Tie." The model must choose between Player A (1), Player B (2), or Tie (3).
The labels are evenly distributed across the three possible outcomes.
Model Performance
| Model | Acc | F1 |
|---|---|---|
| Llama3.1-8b | 0.808 | 0.812 |
| Llama3-8b | 0.775 | 0.778 |
| Solar-10.7b | 0.754 | 0.759 |
| Qwen-8b | 0.783 | 0.788 |
| Yi-chat-9b (TOP) | 0.840 | 0.843 |
Models with fewer than 12 billion parameters were used due to GPU limitations. 😂
About the Labels
The labels in the dataset represent the correct outcome for each round:
- Player A wins (1)
- Player B wins (2)
- Tie (3)
The labels are evenly distributed among the three outcomes to ensure balance.
Stay Tuned
Look forward to the next series in the SquidGame dataset!
(Evaluation code will be uploaded soon.)