Two-Token Features and Small-Large Ensembles for VLM Hallucination Detection
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Computer Science > Computation and Language
Title:Two-Token Features and Small-Large Ensembles for VLM Hallucination Detection
Abstract:We present our system for the SHROOM-Visions 2026 shared task on character-level VLM hallucination detection. A small ($4$B-parameter) VLM is fine-tuned as a per-token classifier reading a two-token feature from its own hidden states, and is ensembled with a $\sim$400B zero-shot VLM judge at prediction time. Both components see off-the-shelf OCR of any visible in-image text. We use synthetic hallucination data generated by the large model as a source of ensemble diversity, and use validation to select feature layer, training data and OCR grounding. Our official entry reaches mean Cor $0.487$ / Cor-lbl $0.387$ on the hidden test set, placing $6$th/$28$ (EN), $6$th/$21$ (FR), $8$th/$21$ (IT) and $7$th/$22$ (ZH) on the task's primary Cor-lbl metric.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.10244 [cs.CL] |
| (or arXiv:2609.10244v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.10244
arXiv-issued DOI via DataCite (pending registration)
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