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[arXiv]
[bibtex]@InProceedings{Jiang_2024_CVPR, author = {Jiang, Chaoya and Xu, Haiyang and Dong, Mengfan and Chen, Jiaxing and Ye, Wei and Yan, Ming and Ye, Qinghao and Zhang, Ji and Huang, Fei and Zhang, Shikun}, title = {Hallucination Augmented Contrastive Learning for Multimodal Large Language Model}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {27036-27046} }
Hallucination Augmented Contrastive Learning for Multimodal Large Language Model
Abstract
Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However MLLMs still face a fundamental limitation of hallucinations where they tend to generate erroneous or fabricated information. In this paper we address hallucinations in MLLMs from a novel perspective of representation learning. We first analyzed the representation distribution of textual and visual tokens in MLLM revealing two important findings: 1) there is a significant gap between textual and visual representations indicating unsatisfactory cross-modal representation alignment; 2) representations of texts that contain and do not contain hallucinations are entangled making it challenging to distinguish them. These two observations inspire us with a simple yet effective method to mitigate hallucinations. Specifically we introduce contrastive learning into MLLMs and use text with hallucination as hard negative examples naturally bringing representations of non-hallucinative text and visual samples closer while pushing way representations of non-hallucinating and hallucinative text. We evaluate our method quantitatively and qualitatively showing its effectiveness in reducing hallucination occurrences and improving performance across multiple benchmarks. On the MMhal-Bench benchmark our method obtains a 34.66% /29.5% improvement over the baseline MiniGPT-4/LLaVA. Our code is available on https://github.com/X-PLUG/mPLUG-HalOwl/tree/main/hacl.
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