training-data-deduplication-improves-llm-performance
IN premise — summaries/2026/08/24/wiki-Large_language_model-chunk-5.md
Created 2026-08-24T17:11:17+00:00
Deduplication of training data measurably improves LLM performance, as demonstrated by Lee et al. (2022), making it a standard data-engineering practice
Summary
Removing repeated examples from a model's training set consistently produces better results, meaning that simply having more data is not the same as having more useful data. This is treated as a well-established, non-negotiable step in building and fine-tuning models, so any pipeline that skips it is working with a known weakness.