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GPT Store Mining and Analysis

2024-05-16 · Dongxun Su, Yanjie Zhao, Xinyi Hou, ShenAo Wang, Haoyu Wang

As a pivotal extension of the renowned ChatGPT, the GPT Store serves as a dynamic marketplace for various Generative Pre-trained Transformer (GPT) models, shaping the frontier of conversational AI. This paper presents an in-depth measurement study of the GPT Store, with a focus on the categorization of GPTs by topic, factors influencing GPT popularity, and the potential security risks. Our investigation starts with assessing the categorization of GPTs in the GPT Store, analyzing how they are organized by topics, and evaluating the effectiveness of the classification system. We then examine the factors that affect the popularity of specific GPTs, looking into user preferences, algorithmic influences, and market trends. Finally, the study delves into the security risks of the GPT Store, identifying potential threats and evaluating the robustness of existing security measures. This study offers a detailed overview of the GPT Store's current state, shedding light on its operational dynamics and user interaction patterns. Our findings aim to enhance understanding of the GPT ecosystem, providing valuable insights for future research, development, and policy-making in generative AI.

📄 PDF Abstract BibTeX arXiv:2405.10210

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Attention 설명 없음
Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…
Weight Decay 설명 없음
Cosine Annealing Cosine Annealing is a type of learning rate schedule that has the effect of starting with a large learning rate that is relatively rapidly decreased to a minimum value before…
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Position-Wise Feed-Forward Layer 설명 없음
Linear Warmup With Cosine Annealing Linear Warmup With Cosine Annealing is a learning rate schedule where we increase the learning rate linearly for $n$ updates and then anneal according to a cosine schedule…

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