Create README.md

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-generation
+- image-text-to-text
+language:
+- en
+tags:
+- machine-unlearning
+- multimodal
+- benchmark
+- evaluation
+- privacy
+- llm
+- vlm
+- neurips
+pretty_name: Multimodal Unlearning Evaluation Benchmark
+size_categories:
+- n<1K
+---
+# 🧠 Multimodal Unlearning Evaluation Benchmark
+
+## 📌 Overview
+This dataset provides evaluation outputs for studying **metric inconsistency in multimodal machine unlearning**.
+
+It supports reproducibility of results in:
+> *Metric Unreliability in Multimodal Machine Unlearning (NeurIPS 2026)*
+
+---
+
+## 📊 Contents
+
+| File | Description |
+|------|------------|
+| 📄 `multimodal_results.json` | Results on VQA benchmarks (MLLMU, UnLOK, MMUBench) |
+| 📄 `unimodal_results.json` | CIFAR-10 baseline results |
+| ⚖️ `uqs_weights.json` | Learned weights for Unified Quality Score (UQS) |
+| 🏆 `ranking_table.json` | Method rankings across metrics |
+| 📈 `analysis_results.json` | Correlation + disagreement analysis |
+| 🔍 `kr_pilot_results.json` | Knowledge Recoverability (KR) pilot results |
+| 🤖 `blip2_minimal_summary.json` | Cross-architecture validation (BLIP-2) |
+
+---
+
+## 🎯 Purpose
+
+This benchmark evaluates five standard unlearning metrics:
+
+- Forget Accuracy (FA)
+- Retain Accuracy (RA)
+- Membership Inference Attack (MIA)
+- Activation Distance (AD)
+- JS Divergence (JS)
+
+⚠️ Key finding:
+> These metrics produce **conflicting rankings** and do not measure **knowledge recoverability (KR)**.
+
+---
+
+## ⚙️ Usage
+
+All results in the paper can be reproduced directly from these files.
+
+Example:
+
+```python
+import json
+
+with open("multimodal_results.json") as f:
+    data = json.load(f)
+
+
+📚 Source Datasets (Not Included)
+
+This benchmark builds on:
+
+MLLMU-Bench
+UnLOK-VQA
+MMUBench
+CIFAR-10
+
+These datasets are not redistributed here. Please refer to their original sources.
+
+⚖️ License
+
+This dataset is released under the CC-BY-4.0 License.
+
+⚠️ Notes
+This dataset contains evaluation outputs, not raw training data
+Designed for benchmarking and reproducibility
+Prepared to support anonymous peer review
+🔗 Citation
+Anonymous. Metric Unreliability in Multimodal Machine Unlearning. NeurIPS 2026.
+
+---