---
title: "Who cites Protecting Privacy in Multimodal — 19 citing papers"
description: "19 papers of the Elamur Research library cite «Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/liu-2025-protecting-privacy-multimodal-large-language/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/liu-2025-protecting-privacy-multimodal-large-language/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Protecting Privacy in Multimodal Large Language…](https://md.research.elamur.ai/free/en/papers/liu-2025-protecting-privacy-multimodal-large-language/) / cited by

# Who cites «Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench»

19 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · [← back to «Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench»](https://md.research.elamur.ai/free/en/papers/liu-2025-protecting-privacy-multimodal-large-language/)

1. **[Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2025-modality-aware-neuron-pruning-unlearning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zheyuan Liu, Guangyao Dou, Xiangchi Yuan, et al.*2025 · 12 citations*
2. **[OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2026-offside-benchmarking-unlearning-misinformation-multimodal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hao Zheng, Zirui Pang, Ling Li, et al.*2026 · 4 citations*
3. **[HearSay Benchmark: Do Audio LLMs Leak What They Hear?](https://md.research.elamur.ai/free/en/papers/wang-2026-hearsay-benchmark-do-audio-llms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jin Wang, Kaiwen Luo, Liang Lin, et al.*2026*
4. **[LOTUS: Evolving Multimodal Unlearning via Hyperbolic Entailment and Lorentz Transport](https://md.research.elamur.ai/free/en/papers/wang-2026-lotus-evolving-multimodal-unlearning-hyperbolic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zekun Wang, Jingjie Zeng, Yingxu Li, et al.*2026*
5. **[Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks](https://md.research.elamur.ai/free/en/papers/sarwar-2026-multimodal-unlearning-across-vision-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu, et al.*2026*
6. **[UMMF: Protecting Copyright of Large Vision-Language Models through Unlearning-based Multimodal Memorization Fingerprint](https://md.research.elamur.ai/free/en/papers/zheng-2026-ummf-protecting-copyright-large-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaofan Zheng, Xinghao Wang, Xiaojun Wan*2026*

13 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/liu-2025-protecting-privacy-multimodal-large-language/) — Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench