Is a Document Educational or Just Wikipedia-Style? -- Pitfalls of Classifier-Based Quality Filtering
This reveals a critical vulnerability in classifier-based quality filtering for LLM pre-training, which is widely used but can be easily bypassed.
The paper shows that a Wikipedia-style reformatting operation can cause the FineWeb-Edu classifier-based quality filter to reverse its decision for about 7% of documents, allowing low-quality content into pre-training corpora.
Classifier-based Quality Filtering has recently emerged as a fundamental technique in constructing pre-training corpora. The ability to deploy a single model that can replace or supplement a set of heuristics has proven effective across numerous Large Language Models. In this work, we expose a critical vulnerability in this approach by demonstrating how a straightforward Wikipedia-style reformatting operation can substantially alter a model's quality assessment and enable low-quality content to surpass filtering thresholds. Our analysis reveals that the FineWeb-Edu CQF model would reverse its filtering decision for approximately 7% of evaluated documents, thereby admitting content into the pre-training corpus that would otherwise have been excluded.