Measuring Agreeableness Bias in Multimodal Models
Abstract
This paper examines a phenomenon in multimodal language models where pre-marked options in question images can significantly influence model responses. Our study employs a systematic methodology to investigate this effect: we present models with images of multiple-choice questions, which they initially answer correctly, then expose the same model to versions with pre-marked options. Our findings reveal a significant shift in the models' responses towards the pre-marked option, even when it contradicts their answers in the neutral settings. Comprehensive evaluations demonstrate that this agreeableness bias is a consistent and quantifiable behavior across various model architectures. These results show potential limitations in the reliability of these models when processing images with pre-marked options, raising important questions about their application in critical decision-making contexts where such visual cues might be present.
Cite
@article{arxiv.2408.09111,
title = {Measuring Agreeableness Bias in Multimodal Models},
author = {Jaehyuk Lim and Bruce W. Lee},
journal= {arXiv preprint arXiv:2408.09111},
year = {2024}
}