Improving Vision-LLMs with Human Cognitive Signals
Angela Lopez Cardona · Sergi Abadal · Ioannis Arapakis
Abstract
We propose to collect eye-tracking during preference annotation to build a Vision–Large Language Model (VLLM) alignment dataset and to train a saliency predictor of human attention. We will compare real versus synthetic gaze supervision and evaluate attention editing together with Direct Preference Optimization (DPO) to reduce hallucinations and improve human alignment.
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