@inproceedings{li-etal-2025-mitigating-object,
title = "Mitigating Object Hallucinations in {MLLM}s via Multi-Frequency Perturbations",
author = "Li, Shuo and
Sun, Jiajun and
Zheng, Guodong and
Fan, Xiaoran and
Shen, Yujiong and
Lu, Yi and
Xi, Zhiheng and
Yang, Yuming and
Tan, Wenming and
Ji, Tao and
Gui, Tao and
Zhang, Qi and
Huang, Xuanjing",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.64/",
pages = "1230--1247",
ISBN = "979-8-89176-335-7",
abstract = "Recently, multimodal large language models (MLLMs) have demonstrated remarkable performance in visual-language tasks. However, the authenticity of the responses generated by MLLMs is often compromised by object hallucinations. We identify that a key cause of these hallucinations is the model{'}s over-susceptibility to image frequency features in detecting objects. In this paper, we introduce Multi-Frequency Perturbations (MFP), a simple, cost-effective, and pluggable adversarial training method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference, thereby mitigating hallucinations. Experimental results demonstrate that our method significantly mitigates object hallucinations across various model architectures. Furthermore, as a training-time method, MFP can be combined with inference-time methods to achieve state-of-the-art performance on the CHAIR benchmark."
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<abstract>Recently, multimodal large language models (MLLMs) have demonstrated remarkable performance in visual-language tasks. However, the authenticity of the responses generated by MLLMs is often compromised by object hallucinations. We identify that a key cause of these hallucinations is the model’s over-susceptibility to image frequency features in detecting objects. In this paper, we introduce Multi-Frequency Perturbations (MFP), a simple, cost-effective, and pluggable adversarial training method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference, thereby mitigating hallucinations. Experimental results demonstrate that our method significantly mitigates object hallucinations across various model architectures. Furthermore, as a training-time method, MFP can be combined with inference-time methods to achieve state-of-the-art performance on the CHAIR benchmark.</abstract>
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%0 Conference Proceedings
%T Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations
%A Li, Shuo
%A Sun, Jiajun
%A Zheng, Guodong
%A Fan, Xiaoran
%A Shen, Yujiong
%A Lu, Yi
%A Xi, Zhiheng
%A Yang, Yuming
%A Tan, Wenming
%A Ji, Tao
%A Gui, Tao
%A Zhang, Qi
%A Huang, Xuanjing
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F li-etal-2025-mitigating-object
%X Recently, multimodal large language models (MLLMs) have demonstrated remarkable performance in visual-language tasks. However, the authenticity of the responses generated by MLLMs is often compromised by object hallucinations. We identify that a key cause of these hallucinations is the model’s over-susceptibility to image frequency features in detecting objects. In this paper, we introduce Multi-Frequency Perturbations (MFP), a simple, cost-effective, and pluggable adversarial training method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference, thereby mitigating hallucinations. Experimental results demonstrate that our method significantly mitigates object hallucinations across various model architectures. Furthermore, as a training-time method, MFP can be combined with inference-time methods to achieve state-of-the-art performance on the CHAIR benchmark.
%U https://aclanthology.org/2025.findings-emnlp.64/
%P 1230-1247
Markdown (Informal)
[Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations](https://aclanthology.org/2025.findings-emnlp.64/) (Li et al., Findings 2025)
ACL
- Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, and Xuanjing Huang. 2025. Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 1230–1247, Suzhou, China. Association for Computational Linguistics.