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arXiv 2503.23213cs.CLcs.AIcs.LG
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RECALL-MM：一个面向风险分析的多模态消费品召回数据集，结合计算方法与大型语言模型

RECALL-MM: A Multimodal Dataset of Consumer Product Recalls for Risk Analysis using Computational Methods and Large Language Models

University of California Berkeley（加州大学伯克利分校）；Autodesk Research（欧特克研究中心）

Diana Bolanos, Mohammadmehdi Ataei, Daniele Grandi, Kosa Goucher-Lambert

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AI总结：

该研究构建多模态召回数据集RECALL-MM，结合聚类分析与大语言模型，实现产品风险识别与设计安全预测，为数据驱动的工程设计提供支持。

摘要：

产品召回为工程设计过程中的潜在风险和危害提供了宝贵的见解，但其全部潜力仍未得到充分利用。在本研究中，我们从美国消费品安全委员会（CPSC）的召回数据库中整理数据，开发了一个多模态数据集RECALL-MM，该数据集利用历史信息为数据驱动的风险评估提供支持，并通过生成方法进行增强。数据集中的模式凸显了改进安全措施可能产生显著影响的特定领域。我们通过展示交互式聚类图来扩展分析，这些聚类图基于召回描述和产品名称将所有召回嵌入到一个共享的潜在空间中。利用这些数据驱动的工具，我们探讨了三个案例研究，以展示该数据集在识别产品风险和指导更安全设计决策方面的实用性。前两个案例研究说明了设计师如何可视化召回产品中的模式，并将新产品创意置于更广泛的召回格局中，以主动预见危害。在第三个案例研究中，我们扩展了方法，采用大型语言模型（LLM）仅基于产品图像来预测潜在危害。这证明了模型利用视觉上下文识别风险因素的能力，揭示了在许多危害类别中与历史召回数据的强烈一致性。然而，分析也强调了危害预测仍具挑战性的领域，凸显了在整个设计过程中风险意识的重要性。总的来说，这项工作旨在弥合历史召回数据与未来产品安全之间的差距，提出一种可扩展的、数据驱动的更安全工程设计方法。

英文摘要：

Product recalls provide valuable insights into potential risks and hazards within the engineering design process, yet their full potential remains underutilized. In this study, we curate data from the United States Consumer Product Safety Commission (CPSC) recalls database to develop a multimodal dataset, RECALL-MM, that informs data-driven risk assessment using historical information, and augment it using generative methods. Patterns in the dataset highlight specific areas where improved safety measures could have significant impact. We extend our analysis by demonstrating interactive clustering maps that embed all recalls into a shared latent space based on recall descriptions and product names. Leveraging these data-driven tools, we explore three case studies to demonstrate the dataset's utility in identifying product risks and guiding safer design decisions. The first two case studies illustrate how designers can visualize patterns across recalled products and situate new product ideas within the broader recall landscape to proactively anticipate hazards. In the third case study, we extend our approach by employing a large language model (LLM) to predict potential hazards based solely on product images. This demonstrates the model's ability to leverage visual context to identify risk factors, revealing strong alignment with historical recall data across many hazard categories. However, the analysis also highlights areas where hazard prediction remains challenging, underscoring the importance of risk awareness throughout the design process. Collectively, this work aims to bridge the gap between historical recall data and future product safety, presenting a scalable, data-driven approach to safer engineering design.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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