# Fast Adaptation with Bradley-Terry Preference Models in Text-To-Image Classification and Generation

Source: [Fast Adaptation with Bradley-Terry Preference Models in Text-To-Image Classification and Generation](<https://www.sota2.com/research/paper/68fa70b41e209131cea4cafb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:26:19.104Z

Authors: Victor Gallego

Published: 2023-07-15T07:53:12.000Z

## Abstract

Recently, large multimodal models, such as CLIP and Stable Diffusion have experimented tremendous successes in both foundations and applications. However, as these models increase in parameter size and computational requirements, it becomes more challenging for users to personalize them for specific tasks or preferences. In this work, we address the problem of adapting the previous models towards sets of particular human preferences, aligning the retrieved or generated images with the preferences of the user. We leverage the Bradley-Terry preference model to develop a fast adaptation method that efficiently fine-tunes the original model, with few examples and with minimal computing resources. Extensive evidence of the capabilities of this framework is provided through experiments in different domains related to multimodal text and image understanding, including preference prediction as a reward model, and generation tasks.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2308.07929>)

[PDF](<https://arxiv.org/pdf/2308.07929.pdf>)