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arXiv · 2606.07653

A Dataset for Dynamic Human Preferences for Vision Language Models

Hannah Gao·Dylan Hadfield-Menell·Rachel Ma
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Abstract

Given the increased adoption of Vision Language Models (VLMs) in human-interactive settings, it is important that we evaluate how well these models can adapt to real-time preferences for different users. While an increasing number of vision-language benchmarks have recently been introduced, they focus largely on evaluating static capabilities and generally-held preferences learned from extensive training data. This work introduces a new benchmark for evaluating the ability of VLMs to understand dynamic human-preferences, i.e. preferences that are passed in-context at inference time. We provide an automated pipeline for generating this benchmark with variations on image dependence, a dynamic multi-modal human-preference dataset, and evaluations of state-of-the-art models on the novel benchmark.

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Hannah Gao, Dylan Hadfield-Menell, Rachel Ma. 2026-06-02. A Dataset for Dynamic Human Preferences for Vision Language Models. https://arxiv.org/abs/2606.07653

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