Create README.md

README.md

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+---
+license: mit
+task_categories:
+- question-answering
+- text-generation
+language:
+- en
+tags:
+- game-theory
+- reasoning
+- connect4
+- synthetic-dataset
+- logic
+pretty_name: Connect4 Reasoning Task
+size_categories:
+- n<1K
+arxiv: 2603.01683
+---
+
+# Dataset Card for Connect4 Reasoning Task
+
+## 1. Dataset Summary
+This dataset is dynamically constructed using the **GAMEBoT** framework in the **Connect4** game. It is designed to evaluate Large Language Models' (LLMs) ability in symbolic reasoning, board state parsing, and lookahead planning. 
+
+By serializing $6 \times 7$ grid states into text-based formats, this dataset challenges models to identify winning topologies in a deterministic environment with a state-space complexity of approximately $4.5 \times 10^{12}$, ensuring a robust evaluation against data contamination.
+
+## 2. Construction Methodology
+- **State Generation**: Two randomized agents simulated gameplay to generate a diverse distribution of board states, covering both balanced and critical tactical scenarios.
+- **Filtering and Balancing**: 
+  - Duplicate states were removed.
+  - To prevent label imbalance (where LLMs might score high by simply predicting "no winning moves"), states with empty answers were downsampled to a **maximum ratio of 20%**.
+- **Ground Truth**: All labels were verified using a perfect solver integrated within the GAMEBoT engine.
+
+## 3. Evaluation Protocol
+For each instance, the model is prompted to solve two sub-problems:
+1. **Self-Winning**: "Are there any potential winning moves to form 4-in-a-row for you? Output all winning moves."
+2. **Opponent-Winning**: "Are there any potential winning moves to form 4-in-a-row for your opponent? Output all winning moves."
+
+The evaluation extracts the final answer from the model's **Chain-of-Thought (CoT)** and compares it against the engine-validated ground truth.
+
+## 4. Citation
+If you find this dataset helpful in your research, please cite the following work:
+
+```bibtex
+@inproceedings{lin2025gamebot,
+  title={GAMEBoT: Transparent assessment of LLM reasoning in games},
+  author={Lin, Wenye and Roberts, Jonathan and Yang, Yunhan and Albanie, Samuel and Lu, Zongqing and Han, Kai},
+  booktitle=ACL,
+  year={2025}
+}