Data Curation Matters: Model Collapse and Spurious Shift Performance Prediction from Training on Uncurated Text Embeddings

• URL: http://arxiv.org/abs/2506.17989v1
• Date: Sun, 22 Jun 2025 11:01:41 GMT
• Title: Data Curation Matters: Model Collapse and Spurious Shift Performance Prediction from Training on Uncurated Text Embeddings
• Authors: Lucas Mattioli, Youness Ait Hadichou, Sabrina Chaouche, Martin Gonzalez,
• Abstract summary: Training models on uncurated Text Embeddings (TEs) can lead to a severe failure mode known as model collapse.<n>We introduce a set of metrics that capture the extent of model collapse, offering a new perspective on TE quality as a proxy for data curation.<n>These findings highlight the need for more nuanced curation and evaluation of embedding-based representations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training models on uncurated Text Embeddings (TEs) derived from raw tabular data can lead to a severe failure mode known as model collapse, where predictions converge to a single class regardless of input. By comparing models trained with identical hyper-parameter configurations on both raw tabular data and their TE-derived counterparts, we find that collapse is a consistent failure mode in the latter setting. We introduce a set of metrics that capture the extent of model collapse, offering a new perspective on TE quality as a proxy for data curation. Our results reveal that TE alone does not effectively function as a curation layer - and that their quality significantly influences downstream learning. More insidiously, we observe that the presence of model collapse can yield artificially inflated and spurious Accuracy-on-the-Line correlation. These findings highlight the need for more nuanced curation and evaluation of embedding-based representations, particularly in out-of-distribution settings.

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