Multimodal Large Language Models (MLLMs) have demonstrated strong performance in visual understanding tasks, yet they often suffer from object hallucinations--generating descriptions of objects that are inconsistent with or entirely absent from the input. This issue is closely related to dataset biases, where frequent co-occurrences of objects lead to entangled semantic representations across modalities. As a result, models may erroneously activate object representations that are commonly associated with the input but not actually present. To address this, we propose a causality-driven disentanglement framework that mitigates hallucinations through causal intervention. Our approach includes a Causal-Driven Projector in the visual pathway and a Causal Intervention Module integrated into the final transformer layer of the language model. These components work together to reduce spurious correlations caused by biased training data. Experimental results show that our method significantly reduces hallucinations while maintaining strong performance on multiple multimodal benchmarks. Visualization analyses further confirm improved separability of object representations. The code is available at: https://github.com/IgniSavium/Causal-LLaVA
@article{arxiv.2505.19474,
title = {Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language Models},
author = {Xinmiao Hu and Chun Wang and Ruihe An and ChenYu Shao and Xiaojun Ye and Sheng Zhou and Liangcheng Li},
journal= {arXiv preprint arXiv:2505.19474},
year = {2025}
}