Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework

Researchers propose a Self-Validation Framework to address object hallucination in Large Vision Language Models (LVLMs), where models generate descriptions of non-existent objects in images. The training-free approach validates object existence through language-prior-free verification and achieves 65.6% improvement on benchmark metrics, suggesting a novel path to enhance LVLM reliability without additional training.

Object hallucination in LVLMs represents a fundamental reliability challenge that undermines their deployment in real-world applications requiring accurate visual understanding. The paper identifies a specific mechanism driving this problem: as generation length increases, models progressively rely more heavily on learned language patterns rather than visual evidence, inflating the probability of hallucinating non-existent objects. This finding adds nuance to previous explanations focused solely on logits calibration.

The research builds on growing recognition that vision-language models exhibit systematic biases toward language priors. Prior work attempted to resolve this through probability recalibration techniques, but lacked deeper mechanistic understanding. This paper's contribution lies in both diagnosing the over-reliance phenomenon empirically and proposing a practical solution that operates within existing model constraints.

The Self-Validation Framework's training-free nature carries significant practical implications. Rather than retraining or fine-tuning models, the approach validates candidate captions by querying the model's visual understanding directly, then selects or aggregates outputs to minimize hallucination. The 65.6% improvement on the CHAIR metric—a standard hallucination benchmark—suggests meaningful progress toward more trustworthy systems.

For developers and organizations deploying LVLMs in production, this framework offers immediate utility by leveraging existing model capabilities without requiring computational overhead or custom training. The approach's success indicates that hallucination mitigation need not demand architectural changes or additional training data, opening possibilities for retrofitting current deployments. Future work should examine how this validation approach scales to different model sizes and whether similar mechanisms apply to other hallucination types beyond object generation.

• →Object hallucination in LVLMs stems from increased reliance on language priors as generation length increases, not just from logits miscalibration.
• →A training-free Self-Validation Framework achieves 65.6% improvement on hallucination metrics by verifying object existence without visual priors.
• →The approach validates candidate captions through language-prior-free verification, then selects or aggregates outputs to reduce hallucination.
• →This method unlocks inherent model potential rather than requiring retraining, enabling immediate deployment in existing systems.
• →The framework demonstrates that hallucination mitigation can occur through intelligent inference-time strategies without architectural modifications.