Add Dataset card and documentation

README.md

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+
+---
+language:
+- en
+license: mit
+size_categories:
+- 10K<n<100K
+task_categories:
+- question-answering
+- text-analysis
+tags:
+- knowledge-coupling
+- llama2
+- hotpotqa
+- multi-hop-reasoning
+- gradient-analysis
+- ripple-effects
+---
+
+# Knowledge Coupling Analysis on HotpotQA Dataset
+
+## Dataset Description
+
+This dataset contains the results of a comprehensive knowledge coupling analysis performed on the HotpotQA dataset using LLaMA2-7B model. The analysis investigates how different pieces of knowledge interact within the model's parameter space through gradient-based coupling measurements.
+
+## Research Overview
+
+- **Model**: meta-llama/Llama-2-7b-hf (layers 28-31 focused analysis)
+- **Dataset**: HotpotQA (train + dev splits, 97,852 total samples)
+- **Method**: Gradient-based knowledge coupling via cosine similarity
+- **Target Layers**: model.layers.28-31.mlp.down_proj (semantically rich layers)
+
+## Key Findings
+
+The analysis revealed:
+- Mean coupling score: 0.0222 across all knowledge piece pairs
+- High coupling pairs (≥0.4 threshold): Critical for ripple effect prediction
+- Layer-specific analysis focusing on MLP down-projection layers
+- Comprehensive gradient analysis with 180,355,072 dimensions per knowledge piece
+
+## Files Description
+
+### Core Results
+- `global_analysis_results.json`: Comprehensive analysis summary with statistics
+- `all_knowledge_pieces.json`: Complete set of processed knowledge pieces (92MB)
+- `all_coupling_pairs.csv`: All pairwise coupling measurements (245MB)
+
+### Supporting Files
+- `dataset_info.json`: Dataset statistics and conversion details
+- `coupling_analysis_config.json`: Analysis configuration and parameters
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the knowledge coupling results
+dataset = load_dataset("your-username/hotpotqa-knowledge-coupling")
+
+# Access global analysis results
+global_results = dataset["global_analysis"]
+
+# Access knowledge pieces
+knowledge_pieces = dataset["knowledge_pieces"]
+
+# Access coupling pairs
+coupling_pairs = dataset["coupling_pairs"]
+```
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{hotpotqa_knowledge_coupling,
+  title={Knowledge Coupling Analysis on HotpotQA Dataset using LLaMA2-7B},
+  author={[Your Name]},
+  year={2024},
+  publisher={HuggingFace},
+  url={https://huggingface.co/datasets/your-username/hotpotqa-knowledge-coupling}
+}
+```
+
+## Technical Details
+
+- **Gradient Computation**: ∇_θ log P(answer|question) for cloze-style questions
+- **Coupling Measurement**: Cosine similarity between L2-normalized gradients
+- **Memory Optimization**: Focused on layers 28-31 to handle GPU memory constraints
+- **Hardware**: NVIDIA A40 GPU (46GB VRAM)
+
+## License
+
+This dataset is released under the MIT License. The original HotpotQA dataset follows its respective licensing terms.