Summary of Lshbloom: Memory-efficient, Extreme-scale Document Deduplication, by Arham Khan et al.
LSHBloom: Memory-efficient, Extreme-scale Document Deduplication
by Arham Khan, Robert Underwood, Carlo Siebenschuh, Yadu Babuji, Aswathy Ajith, Kyle Hippe, Ozan Gokdemir, Alexander Brace, Kyle Chard, Ian Foster
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach, LSHBloom, is an extension to MinhashLSH that improves the efficiency of document-level deduplication for large language models (LLMs). The main challenge addressed in this paper is the detection and elimination of duplicate content in training datasets, which can lead to increased costs, memorization, or cheating on evaluation. LSHBloom replaces the expensive LSHIndex with lightweight Bloom filters, achieving similar deduplication performance as MinhashLSH but with a marginal increase in false positives (as low as 1e-5). The method also demonstrates competitive runtime (270% faster than MinhashLSH) and uses significantly less disk space (0.6% of the required space for MinhashLSH). The paper shows that this approach scales well with increased dataset size, promising a 250% speedup and a 54x space advantage over traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need training datasets without duplicates to avoid unwanted properties like memorization or cheating. But finding these duplicates is a big problem! Traditional methods are very slow and use lots of computer memory. The new approach, LSHBloom, solves this problem by making the process faster and using less space. It’s like a filter that helps find duplicate documents quickly and efficiently. |
