--- title: "Who cites Safety Fine-Tuning at (Almost) No — 56 citing papers" description: "56 papers of the Elamur Research library cite «Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models»…" canonical: "https://research.elamur.ai/free/en/papers/zong-2024-safety-fine-tuning-almost-no/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zong-2024-safety-fine-tuning-almost-no/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Safety Fine-Tuning at (Almost) No Cost: A…](https://md.research.elamur.ai/free/en/papers/zong-2024-safety-fine-tuning-almost-no/) / cited by # Who cites «Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models» 56 papers of our library cite it (of the 149,439 whose bibliographies we read) · 17 with a page here, the most cited first · [← back to «Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models»](https://md.research.elamur.ai/free/en/papers/zong-2024-safety-fine-tuning-almost-no/) 1. [**Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone arXiv:2404.14219**Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.*2024 · 1,134 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/) 2. **[MM-LLMs: Recent Advances in MultiModal Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2024-mm-llms-recent-advances-multimodal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Duzhen Zhang, Yahan Yu, Jiahua Dong, et al.*2024 · 320 citations* 3. [**Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs arXiv:2503.01743**Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, et al.*2025 · 152 citations*](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/) 4. **[VLSBench: Unveiling Visual Leakage in Multimodal Safety](https://md.research.elamur.ai/free/en/papers/hu-2025-vlsbench-unveiling-visual-leakage-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuhao Hu, Dongrui Liu, Hao Li, et al.*2025 · 32 citations* 5. **[HiddenDetect: Detecting Jailbreak Attacks against Multimodal Large Language Models via Monitoring Hidden States](https://md.research.elamur.ai/free/en/papers/jiang-2025-hiddendetect-detecting-jailbreak-attacks-against/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yilei Jiang, Xinyan Gao, Tianshuo Peng, et al.*2025 · 15 citations* 6. **[Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment](https://md.research.elamur.ai/free/en/papers/li-2025-layer-aware-representation-filtering-purifying/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hao Li, Lijun Li, Zhenghao Lu, et al.*2025 · 11 citations* 7. **[Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring](https://md.research.elamur.ai/free/en/papers/hua-2026-rethinking-jailbreak-detection-large-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peichun Hua, Hao Li, Shanghao Shi, et al.*2026 · 10 citations* 8. **[Visual Contextual Attack: Jailbreaking MLLMs with Image-Driven Context Injection](https://md.research.elamur.ai/free/en/papers/ziqi-2025-visual-contextual-attack-jailbreaking-mllms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Miao Ziqi, Yi Ding, Lijun Li, et al.*2025 · 6 citations* 9. **[LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint](https://md.research.elamur.ai/free/en/papers/ma-2025-led-merging-mitigating-safety-utility/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qianli Ma, Dongrui Liu, Qian Chen, et al.*2025 · 5 citations* 10. **[CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning](https://md.research.elamur.ai/free/en/papers/yi-2026-ctrap-embedding-collapse-trap-safeguard/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Biao Yi, Tiansheng Huang, Baolei Zhang, et al.*2026 · 3 citations* 11. **[USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS](https://md.research.elamur.ai/free/en/papers/zheng-2026-usb-comprehensive-unified-safety-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Baolin Zheng, Guanlin Chen, Qingyang Teng, et al.*2026 · 1 citation* 12. **[When Helpers Become Hazards: A Benchmark for Analyzing Multimodal LLM-Powered Safety in Daily Life](https://md.research.elamur.ai/free/en/papers/lou-2026-when-helpers-become-hazards-benchmark/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinyue Lou, Xu Jinan, Jingyi Yin, et al.*2026 · 1 citation* 13. **[CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks](https://md.research.elamur.ai/free/en/papers/zhang-2026-crossguard-safeguarding-mllms-against-joint/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xu Zhang, Hao Li, Zhichao Lu*2026* 14. **[OASIS: Mitigating Harmful Fine-tuning Attacks on LLMs via Orthogonal and Adaptive Safety Alignment Strategy](https://md.research.elamur.ai/free/en/papers/tang-2026-oasis-mitigating-harmful-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiayu Tang, Guowei Peng, Qiuhao Xie, et al.*2026* 15. **[ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation](https://md.research.elamur.ai/free/en/papers/zhang-2026-reasonrec-reasoning-augmented-multimodal-agent/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yihua Zhang, Mingfu Liang, Jiyan Yang, et al.*2026* 16. **[SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia](https://md.research.elamur.ai/free/en/papers/tasawong-2026-sea-guard-culturally-grounded-multilingual/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Panuthep Tasawong, Jian Gang Ngui, Alham Fikri Aji, et al.*2026* 17. **[Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images](https://md.research.elamur.ai/free/en/papers/yang-2026-visual-self-fulfilling-alignment-shaping/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qishun Yang, Shu Yang, Lijie Hu, et al.*2026* 39 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zong-2024-safety-fine-tuning-almost-no/) — Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models