Datasets:
Detecting synthetic / template content in web corpora — what signals work at scale?
As more AI-generated and template-heavy content ends up in web crawls, identifying and filtering it becomes a real challenge for large pretraining corpora.
Curious how others have approached this, particularly:
1. Feature combination — Single signals (n-gram repetition, vocabulary diversity, sentence length variance) tend to have too many false positives on their own. Did Dolma's pipeline combine multiple signals? How were they weighted or thresholded?
2. Machine-translated content — Pages translated by MT systems often pass fluency and language identification checks. They tend to show phrase-boundary artifacts and low syntactic variety, but these are subtle. Was there anything in Dolma's pipeline specifically targeting translated content, or was it caught by general quality filters?
3. Calibration across data sources — Common Crawl, Wikipedia, and books have very different baseline characteristics. Did you calibrate slop detection separately per source, or apply a single cross-source threshold?
In our work on a Swiss multilingual web corpus (.ch domains, DE/FR/EN/IT), we ended up computing a composite score from four signals: vocabulary diversity, sentence length variance, n-gram repetition rate, and capitalization pattern deviation. Documents above threshold were tiered separately rather than hard-discarded — the signal was too noisy for binary filtering, and some template-heavy documents still contained useful factual content.
The harder problem was cross-language calibration. German text has structurally lower vocabulary diversity than French due to compound words and inflection, so thresholds calibrated on English or French consistently over-flagged German content until we separated them.
For context on our approach: https://huggingface.co/datasets/OptiTransferData/swiss-web-premium-ch
Interested in what others have found, particularly at the scale Dolma operates at.