Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning

• URL: http://arxiv.org/abs/2506.07227v1
• Date: Sun, 08 Jun 2025 17:23:36 GMT
• Title: Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning
• Authors: Tianyi Bai, Yuxuan Fan, Jiantao Qiu, Fupeng Sun, Jiayi Song, Junlin Han, Zichen Liu, Conghui He, Wentao Zhang, Binhang Yuan,
• Abstract summary: Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks but struggle with fine-grained visual differences.<n>We propose a controlled data generation pipeline that produces minimally edited image pairs with semantically aligned captions.
• Score: 27.33722610773045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks but still struggle with fine-grained visual differences, leading to hallucinations or missed semantic shifts. We attribute this to limitations in both training data and learning objectives. To address these issues, we propose a controlled data generation pipeline that produces minimally edited image pairs with semantically aligned captions. Using this pipeline, we construct the Micro Edit Dataset (MED), containing over 50K image-text pairs spanning 11 fine-grained edit categories, including attribute, count, position, and object presence changes. Building on MED, we introduce a supervised fine-tuning (SFT) framework with a feature-level consistency loss that promotes stable visual embeddings under small edits. We evaluate our approach on the Micro Edit Detection benchmark, which includes carefully balanced evaluation pairs designed to test sensitivity to subtle visual variations across the same edit categories. Our method improves difference detection accuracy and reduces hallucinations compared to strong baselines, including GPT-4o. Moreover, it yields consistent gains on standard vision-language tasks such as image captioning and visual question answering. These results demonstrate the effectiveness of combining targeted data and alignment objectives for enhancing fine-grained visual reasoning in MLLMs.

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