TruthLens: Object Hallucination Detection via Self-Evaluating Truthfulness Scores in LVLMs

Despite the remarkable progress of large vision languagemodels (LVLMs), object hallucination remains a fundamental challengethat hinders their trustworthy deployment. A key finding motivates ourwork: real and hallucinated object tokens are clearly separable in hid-den representations, yet this separability is largely lost at the language-modeling (LM) head. We propose TruthLens, a self-evaluation frame-work that teaches the LM head to expose a per-object truthfulness sig-nal without any auxiliary model or additional inference cost. Concretely,a rarely-used special token is repurposed as a reference token. For eachobject-token position, we extract the log-probability assigned to this spe-cial token by the LM head, and define its difference from a predefinedconstant as the truthfulness score. The model is then fine-tuned withan MSE objective that drives scores toward 1 for real objects and 0for hallucinated ones, while a divergence constraint preserves the origi-nal generation capability. Despite being trained on only a limited set ofobject categories, TruthLens generalizes effectively to benchmarks withsubstantially larger label spaces. Extensive experiments across multipleLVLMs demonstrate state-of-the-art performance; notably, on Qwen2.5-VL-7B, TruthLens outperforms the previous best method on MS-COCOby over 17% in AUROC. Our code is available at https://github.com/wyqstan/TruthLens.