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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
configs:
- config_name: multimodal
data_files:
- split: train
path: outputs/multimodal_results.json
- config_name: unimodal
data_files:
- split: train
path: outputs/unimodal_results.json
- config_name: uqs_weights
data_files:
- split: train
path: outputs/uqs_weights.json
- config_name: ranking
data_files:
- split: train
path: outputs/ranking_table.json
- config_name: analysis
data_files:
- split: train
path: outputs/analysis_results.json
- config_name: kr_pilot
data_files:
- split: train
path: outputs/kr_pilot_results.json
- config_name: blip2
data_files:
- split: train
path: outputs/blip2_minimal_summary.json
---
# 🧠 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-Bench, UnLOK-VQA, 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 and 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.
---
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