Behavioral Bias of Vision-Language Models: A Behavioral Finance View

• URL: http://arxiv.org/abs/2409.15256v1
• Date: Mon, 23 Sep 2024 17:54:47 GMT
• Title: Behavioral Bias of Vision-Language Models: A Behavioral Finance View
• Authors: Yuhang Xiao, Yudi Lin, Ming-Chang Chiu,
• Abstract summary: We study the potential behavioral biases of Large Vision-Language Models (LVLMs) from a behavioral finance perspective. Our evaluations find that open-source LVLMs such as LLaVA-NeXT, MobileVLM-V2, Mini-Gemini, Mini-Llama3-V 2.5 and Phi-3-vision-128k suffer significantly from these two biases. Our observations highlight directions in which open-source models can improve.
• Score: 1.7205106391379021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Vision-Language Models (LVLMs) evolve rapidly as Large Language Models (LLMs) was equipped with vision modules to create more human-like models. However, we should carefully evaluate their applications in different domains, as they may possess undesired biases. Our work studies the potential behavioral biases of LVLMs from a behavioral finance perspective, an interdisciplinary subject that jointly considers finance and psychology. We propose an end-to-end framework, from data collection to new evaluation metrics, to assess LVLMs' reasoning capabilities and the dynamic behaviors manifested in two established human financial behavioral biases: recency bias and authority bias. Our evaluations find that recent open-source LVLMs such as LLaVA-NeXT, MobileVLM-V2, Mini-Gemini, MiniCPM-Llama3-V 2.5 and Phi-3-vision-128k suffer significantly from these two biases, while the proprietary model GPT-4o is negligibly impacted. Our observations highlight directions in which open-source models can improve. The code is available at https://github.com/mydcxiao/vlm_behavioral_fin.

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