Chain-of-Thought Prompting for Demographic Inference with Large Multimodal Models

• URL: http://arxiv.org/abs/2405.15687v1
• Date: Fri, 24 May 2024 16:26:56 GMT
• Title: Chain-of-Thought Prompting for Demographic Inference with Large Multimodal Models
• Authors: Yongsheng Yu, Jiebo Luo,
• Abstract summary: Large multimodal models (LMMs) have shown transformative potential across various research tasks. Our findings indicate LMMs possess advantages in zero-shot learning, interpretability, and handling uncurated 'in-the-wild' inputs. We propose a Chain-of-Thought augmented prompting approach, which effectively mitigates the off-target prediction issue.
• Score: 58.58594658683919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional demographic inference methods have predominantly operated under the supervision of accurately labeled data, yet struggle to adapt to shifting social landscapes and diverse cultural contexts, leading to narrow specialization and limited accuracy in applications. Recently, the emergence of large multimodal models (LMMs) has shown transformative potential across various research tasks, such as visual comprehension and description. In this study, we explore the application of LMMs to demographic inference and introduce a benchmark for both quantitative and qualitative evaluation. Our findings indicate that LMMs possess advantages in zero-shot learning, interpretability, and handling uncurated 'in-the-wild' inputs, albeit with a propensity for off-target predictions. To enhance LMM performance and achieve comparability with supervised learning baselines, we propose a Chain-of-Thought augmented prompting approach, which effectively mitigates the off-target prediction issue.

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