Model-in-the-Loop (MILO): Accelerating Multimodal AI Data Annotation with LLMs

• URL: http://arxiv.org/abs/2409.10702v2
• Date: Tue, 24 Sep 2024 05:00:07 GMT
• Title: Model-in-the-Loop (MILO): Accelerating Multimodal AI Data Annotation with LLMs
• Authors: Yifan Wang, David Stevens, Pranay Shah, Wenwen Jiang, Miao Liu, Xu Chen, Robert Kuo, Na Li, Boying Gong, Daniel Lee, Jiabo Hu, Ning Zhang, Bob Kamma,
• Abstract summary: We propose the Model-in-the-Loop (MILO) framework, which integrates AI/ML models into the annotation process. Our research introduces a collaborative paradigm that leverages the strengths of both professional human annotators and large language models (LLMs) Three empirical studies on multimodal data annotation demonstrate MILO's efficacy in reducing handling time, improving data quality, and enhancing annotator experiences.
• Score: 19.331803578031188
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The growing demand for AI training data has transformed data annotation into a global industry, but traditional approaches relying on human annotators are often time-consuming, labor-intensive, and prone to inconsistent quality. We propose the Model-in-the-Loop (MILO) framework, which integrates AI/ML models into the annotation process. Our research introduces a collaborative paradigm that leverages the strengths of both professional human annotators and large language models (LLMs). By employing LLMs as pre-annotation and real-time assistants, and judges on annotator responses, MILO enables effective interaction patterns between human annotators and LLMs. Three empirical studies on multimodal data annotation demonstrate MILO's efficacy in reducing handling time, improving data quality, and enhancing annotator experiences. We also introduce quality rubrics for flexible evaluation and fine-grained feedback on open-ended annotations. The MILO framework has implications for accelerating AI/ML development, reducing reliance on human annotation alone, and promoting better alignment between human and machine values.

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