An overview of the data and tokenization pipeline for training large language models, covering quality and splits.

Tokenization is the process of breaking down a piece of text into smaller units called tokens, which can be words, subwords, or characters. Models process these tokens instead of raw text. Byte-Pair Encoding (BPE) is a popular subword tokenization algorithm. It starts with a vocabulary of individual characters and iteratively merges the most frequent adjacent pair of tokens. This approach is effective because it can represent any word, avoids the issue of 'unknown' words common with word-level tokenizers, and keeps the vocabulary size manageable while capturing common word parts and morphemes, leading to efficient model learning.

Data deduplication is crucial because large-scale datasets, especially those scraped from the web, often contain vast amounts of repeated or near-identical content. Training a model on this redundant data can cause it to overfit, memorizing specific phrases or examples instead of learning general patterns. This hurts its ability to generalize to new, unseen data. Techniques like MinHash and Locality-Sensitive Hashing (LSH) are probabilistic methods that efficiently find and remove these duplicates at scale, ensuring the training data is diverse and representative, which leads to a more robust and capable model.

Data contamination refers to the accidental inclusion of data from evaluation or test sets into the training dataset. If a model is trained on the same data it will be tested on, its performance metrics will be artificially inflated, giving a false impression of its true capabilities. Contamination checks are a critical final step in the data pipeline where the training data is rigorously compared against standard benchmarks and held-out evaluation sets to ensure there is no overlap. This guarantees that the model's performance is evaluated fairly and accurately on genuinely unseen data.