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