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🔥🔥🔥[ICLR 2026 Oral] Hallucination Begins Where Saliency Drops

License: MIT GitHub Stars

The motivation of this paper is

image

The patterns of incorrect and correct tokens are as follows:

image

After intervention, incorrect tokens became correct tokens, and the saliency score increased significantly.

image

For LLaVA1.5

python demo_step1.py,python demo_step2.py

For Qwen2-VL

python Qwen_step1.py,python Qwen_step2.py

Citation

@inproceedings{
zhang-saliency,
title={Hallucination Begins Where Saliency Drops},
author={Xiaofeng Zhang, Yuanchao Zhu, Chaochen Gu, Xiaosong Yuan, Qiyan Zhao, Jiawei Cao, Feilong Tang, Sinan Fan, Yaomin Shen, Chen Shen, Hao Tang },
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=sjnErRHXf3}
}

Acknowledgement

This repo is built on LLaVA (models), Qwen2.5-VL (CHAIR evaluation) and Label words. Many thanks for their efforts. The use of our code should also follow the original licenses.

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[ICLR 2026 Oral] 🎉Hallucination Begins Where Saliency Drops

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