Understanding Generative AI Content with Embedding Models
- URL: http://arxiv.org/abs/2408.10437v3
- Date: Sat, 22 Feb 2025 18:56:49 GMT
- Title: Understanding Generative AI Content with Embedding Models
- Authors: Max Vargas, Reilly Cannon, Andrew Engel, Anand D. Sarwate, Tony Chiang,
- Abstract summary: We show that deep neural networks (DNNs) implicitly engineer features by transforming their input data into hidden feature vectors called embeddings.<n>We find empirical evidence that there is intrinsic separability between real samples and those generated by artificial intelligence (AI)
- Score: 4.662332573448995
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representations based on domain expertise, deep neural networks (DNNs) now offer a radically different approach. DNNs implicitly engineer features by transforming their input data into hidden feature vectors called embeddings. For embedding vectors produced by foundation models -- which are trained to be useful across many contexts -- we demonstrate that simple and well-studied dimensionality-reduction techniques such as Principal Component Analysis uncover inherent heterogeneity in input data concordant with human-understandable explanations. Of the many applications for this framework, we find empirical evidence that there is intrinsic separability between real samples and those generated by artificial intelligence (AI).
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