Alkhder uses 7B vision-language model for multimodal hallucination detection at ImageEval 2026
The authors submitted a system to Subtask 1b of ImageEval 2026, which requires picking the single visually valid statement from three candidates paired with an image. The task is offered in both English and Modern Standard Arabic (MSA). Their approach relies on a pre‑trained 7B vision‑language model applied in a zero‑shot setting, without any task‑specific training or fine‑tuning. They treat the…
Key points
- System uses a pre‑trained 7B vision‑language model in zero‑shot mode.
- CI scores: 0.0510 (English) and 0.0960 (MSA), cutting baseline CI by 83.7% and 80.4%.
- Applies Latin‑square rotation to candidate statements to reduce answer‑position bias.
On the official test sets the system achieved CI scores of 0.0510 for English and 0.0960 for MSA. Compared with the reported 3B baselines on the devtest split, these results represent reductions of 83.7% and 80.4% respectively. The paper demonstrates that a relatively small, off‑the‑shelf vision‑language model can substantially improve multimodal hallucination detection across languages when combined with careful inference strategies.
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