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GPT Store Mining and Analysis

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arxiv 2405.10210 v1 pith:4YDHILUD submitted 2024-05-16 cs.LG cs.SE

classification cs.LGcs.SE
keywords storegptssecuritycategorizationevaluatingfactorsgenerativepopularity
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Understanding the Supply Chain and Risks of Large Language Model Applications

    cs.SE 2025-07 conditional novelty 7.0 of 10

    A new benchmark dataset traces dependencies across 3,859 LLM applications, 109,211 models, 2,474 datasets, and 8,862 libraries, and finds widespread known vulnerabilities in application dependencies.

  2. LaQual: An Automated Framework for LLM App Quality Evaluation

    cs.SE 2025-08 reject novelty 5.0 of 10

    LaQual automates LLM app-store quality evaluation through scenario classification, static indicator filtering, and LLM-generated dynamic metrics, with Spearman correlations of about 0.6 against human ratings.

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