PUFFER: Incremental Fuzzy Deduplication for Continuously Evolving Corpora
Large language model training corpora grow through successive, often redundant releases, so each release must be deduplicated against both itself and the accumulated history. At trillion-token scale, this requires incremental ingestion, bounded resident memory, deterministic retry, and dataset-scoped lifecycle control without repeated corpus-wide rebuilding. We introduce PUFFER (Provenance-aware Updatable Fuzzy Filtering for Evolving Repositories), a MinHash-LSH fuzzy-deduplication pipeline built around two design choices. First, PUFFER stores each LSH band as immutable, dataset-tagged, memory-mapped sorted segments, enabling exact historical band-key membership checks without RAM proport...
| Authors | Xiao Yang, Erik Edward Aldape, Beren Millidge |
|---|---|
| Primary category | cs.DB |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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