---
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.

---