Model-agnostic Coreset Selection via LLM-based Concept Bottlenecks

• URL: http://arxiv.org/abs/2502.16733v1
• Date: Sun, 23 Feb 2025 22:14:42 GMT
• Title: Model-agnostic Coreset Selection via LLM-based Concept Bottlenecks
• Authors: Akshay Mehra, Trisha Mittal, Subhadra Gopalakrishnan, Joshua Kimball,
• Abstract summary: Coreset Selection (CS) identifies a subset of training data that achieves model performance comparable to using the entire dataset.<n>These scores are inefficient to compute and hard to interpret as they do not indicate whether a sample is difficult to learn in general or only for a specific model.<n>Our work proposes an interpretable score that gauges a sample's difficulty using human-understandable textual attributes (concepts) independent of any downstream model.
• Score: 6.857632954159568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coreset Selection (CS) identifies a subset of training data that achieves model performance comparable to using the entire dataset. Many state-of-the-art CS methods, select coresets using scores whose computation requires training the downstream model on the entire dataset and recording changes in its behavior on samples as it trains (training dynamics). These scores are inefficient to compute and hard to interpret as they do not indicate whether a sample is difficult to learn in general or only for a specific model. Our work addresses these challenges by proposing an interpretable score that gauges a sample's difficulty using human-understandable textual attributes (concepts) independent of any downstream model. Specifically, we measure the alignment between a sample's visual features and concept bottlenecks, derived via large language models, by training a linear concept bottleneck layer and compute the sample's difficulty score using it. We then use this score and a stratified sampling strategy to identify the coreset. Crucially, our score is efficiently computable without training the downstream model on the full dataset even once, leads to high-performing coresets for various downstream models, and is computable even for an unlabeled dataset. Through experiments on CIFAR-10, CIFAR-100, and ImageNet-1K, we show our coresets outperform random subsets, even at high pruning rates, and achieve model performance comparable to or better than coresets found by training dynamics-based methods.

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