Measuring Agreeableness Bias in Multimodal Models

• URL: http://arxiv.org/abs/2408.09111v2
• Date: Tue, 15 Oct 2024 02:42:37 GMT
• Title: Measuring Agreeableness Bias in Multimodal Models
• Authors: Jaehyuk Lim, Bruce W. Lee,
• Abstract summary: This paper examines a phenomenon in multimodal language models where pre-marked options in question images can influence model responses. 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.
• Score: 0.3529736140137004
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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