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arXiv · 2510.14330

Ensembling Multiple Hallucination Detectors Trained on VLLM Internal Representations

Yuto Nakamizo·Ryuhei Miyazato·Hikaru Tanabe·Ryuta Yamakura·Kiori Hatanaka
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Abstract

This paper presents the 5th place solution by our team, y3h2, for the Meta CRAG-MM Challenge at KDD Cup 2025. The CRAG-MM benchmark is a visual question answering (VQA) dataset focused on factual questions about images, including egocentric images. The competition was contested based on VQA accuracy, as judged by an LLM-based automatic evaluator. Since incorrect answers result in negative scores, our strategy focused on reducing hallucinations from the internal representations of the VLM. Specifically, we trained logistic regression-based hallucination detection models using both the hidden_state and the outputs of specific attention heads. We then employed an ensemble of these models. As a result, while our method sacrificed some correct answers, it significantly reduced hallucinations and allowed us to place among the top entries on the final leaderboard.

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Yuto Nakamizo, Ryuhei Miyazato, Hikaru Tanabe, Ryuta Yamakura, Kiori Hatanaka. 2025-10-16. Ensembling Multiple Hallucination Detectors Trained on VLLM Internal Representations. https://arxiv.org/abs/2510.14330

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