Prior-based Noisy Text Data Filtering: Fast and Strong Alternative For Perplexity

• URL: http://arxiv.org/abs/2509.18577v2
• Date: Mon, 29 Sep 2025 02:13:38 GMT
• Title: Prior-based Noisy Text Data Filtering: Fast and Strong Alternative For Perplexity
• Authors: Yeongbin Seo, Gayoung Kim, Jaehyung Kim, Jinyoung Yeo,
• Abstract summary: We propose a prior-based data filtering method that estimates token priors using corpus-level term frequency statistics.<n>Our approach filters documents based on the mean and standard deviation of token priors, serving as a fast proxy to PPL.<n>Despite its simplicity, the prior-based filter achieves the highest average performance across 20 downstream benchmarks.
• Score: 16.521507516831097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) are pretrained on massive web corpora, careful selection of data becomes essential to ensure effective and efficient learning. While perplexity (PPL)-based filtering has shown strong performance, it suffers from drawbacks: substantial time costs and inherent unreliability of the model when handling noisy or out-of-distribution samples. In this work, we propose a simple yet powerful alternative: a prior-based data filtering method that estimates token priors using corpus-level term frequency statistics, inspired by linguistic insights on word roles and lexical density. Our approach filters documents based on the mean and standard deviation of token priors, serving as a fast proxy to PPL while requiring no model inference. Despite its simplicity, the prior-based filter achieves the highest average performance across 20 downstream benchmarks, while reducing time cost by over 1000x compared to PPL-based filtering. We further demonstrate its applicability to symbolic languages such as code and math, and its dynamic adaptability to multilingual corpora without supervision

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