Summary of Protecting Privacy in Multimodal Large Language Models with Mllmu-bench, by Zheyuan Liu et al.
Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
by Zheyuan Liu, Guangyao Dou, Mengzhao Jia, Zhaoxuan Tan, Qingkai Zeng, Yongle Yuan, Meng Jiang
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the issue of Large Language Models (LLMs) and Multimodal Large Language models (MLLMs) memorizing confidential data. It proposes a novel benchmark, MLLMU-Bench, to advance understanding of multimodal machine unlearning. The benchmark consists of 653 profiles with customized question-answer pairs, evaluated from multimodal and unimodal perspectives. Four sets assess unlearning algorithms’ efficacy, generalizability, and model utility. Baseline results using existing generative model unlearning algorithms are provided. Surprisingly, experiments show that unimodal unlearning excels in generation and cloze tasks, while multimodal approaches perform better in classification tasks with multimodal inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure language models don’t remember private information. These models can be trained on a lot of data from the internet, which means they might learn personal secrets. The problem has been fixed for one type of model, but not for another type that deals with images and text. To solve this issue, the researchers created a new tool to test how well algorithms can remove unwanted memories from language models. They tested different methods and found that some work better in certain situations. |
Keywords
» Artificial intelligence » Classification » Generative model
