Mitigating Object Hallucinations via Sentence-Level Early Intervention

• URL: http://arxiv.org/abs/2507.12455v2
• Date: Sat, 26 Jul 2025 18:41:27 GMT
• Title: Mitigating Object Hallucinations via Sentence-Level Early Intervention
• Authors: Shangpin Peng, Senqiao Yang, Li Jiang, Zhuotao Tian,
• Abstract summary: Multimodal large language models (MLLMs) have revolutionized cross-modal understanding but continue to struggle with hallucinations.<n>We propose SENTINEL, a framework that eliminates dependency on human annotations.<n>Sentence-level Early iNtervention Through IN-domain prEference Learning can reduce hallucinations by over 90% compared to the original model.
• Score: 10.642552315531404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal large language models (MLLMs) have revolutionized cross-modal understanding but continue to struggle with hallucinations - fabricated content contradicting visual inputs. Existing hallucination mitigation methods either incur prohibitive computational costs or introduce distribution mismatches between training data and model outputs. We identify a critical insight: hallucinations predominantly emerge at the early stages of text generation and propagate through subsequent outputs. To address this, we propose SENTINEL (Sentence-level Early iNtervention Through IN-domain prEference Learning), a framework that eliminates dependency on human annotations. Specifically, we first bootstrap high-quality in-domain preference pairs by iteratively sampling model outputs, validating object existence through cross-checking with two open-vocabulary detectors, and classifying sentences into hallucinated/non-hallucinated categories. Subsequently, we use context-coherent positive samples and hallucinated negative samples to build context-aware preference data iteratively. Finally, we train models using a context-aware preference loss (C-DPO) that emphasizes discriminative learning at the sentence level where hallucinations initially manifest. Experimental results show that SENTINEL can reduce hallucinations by over 90% compared to the original model and outperforms the previous state-of-the-art method on both hallucination benchmarks and general capabilities benchmarks, demonstrating its superiority and generalization ability. The models, datasets, and code are available at https://github.com/pspdada/SENTINEL.

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