用未经清洗的文本嵌入训练模型会引发预测坍缩，导致所有输入都输出同一类。
Data Curation Matters: Model Collapse and Spurious Shift Performance Prediction from Training on Uncurated Text Embeddings
- 通过对比原始数据与文本嵌入训练，发现嵌入训练易引发模型坍塌。
- 模型坍塌时会出现虚假的准确率线性相关，误导评估结果。
- 提出新指标衡量坍塌程度，强调嵌入质量需像数据一样严格清洗。
在由原始表格数据生成的未经清洗文本嵌入（TEs）上训练模型，会导致一种严重失效现象——模型坍塌，即无论输入如何，预测均收敛至单一类别。通过对比相同超参数下基于原始表格数据与相应文本嵌入的模型训练表现，我们发现嵌入训练场景中坍塌是持续出现的失败模式。本文引入一组量化模型坍塌程度的指标，将嵌入质量作为数据清洗效果的新代理指标。结果表明，仅依赖文本嵌入无法有效充当清洗层，其质量显著影响下游学习性能。更危险的是，模型坍塌会引发人为夸大且虚假的‘准确率-线性’相关性。这些发现凸显了在分布外设置下对嵌入表示进行更精细清洗与评估的必要性。
原文摘要 · Abstract (English)
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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