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This paper introduces TruthLens, a self-evaluation framework designed to detect object hallucinations in large vision language models (LVLMs) by leveraging a per-object truthfulness score. By repurposing a rarely-used special token as a reference, the framework fine-tunes the model to assign higher probabilities to real objects while effectively distinguishing them from hallucinated ones, achieving state-of-the-art performance on benchmark datasets. Notably, TruthLens surpasses the previous best method on MS-COCO by over 17% in AUROC, demonstrating its effectiveness even with limited training categories.
TruthLens reveals that a simple self-evaluation mechanism can dramatically enhance the accuracy of object detection in LVLMs, outperforming existing methods by a significant margin.
Despite the remarkable progress of large vision language models (LVLMs), object hallucination remains a fundamental challenge that hinders their trustworthy deployment. A key finding motivates our work: real and hallucinated object tokens are clearly separable in hidden representations, yet this separability is largely lost at the language-modeling (LM) head. We propose TruthLens, a self-evaluation framework that teaches the LM head to expose a per-object truthfulness signal without any auxiliary model or additional inference cost. Concretely, a rarely-used special token is repurposed as a reference token. For each object-token position, we extract the log-probability assigned to this special token by the LM head, and define its difference from a predefined constant as the truthfulness score. The model is then fine-tuned with an MSE objective that drives scores toward 1 for real objects and 0 for hallucinated ones, while a divergence constraint preserves the original generation capability. Despite being trained on only a limited set of object categories, TruthLens generalizes effectively to benchmarks with substantially larger label spaces. Extensive experiments across multiple LVLMs demonstrate state-of-the-art performance; notably, on Qwen2.5-VL-7B, TruthLens outperforms the previous best method on MS-COCO by over 17\% in AUROC. Our code is available at https://github.com/wyqstan/TruthLens.
