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Data cleaning

As explained in LLMs in Production

Data cleaning is the process of detecting, correcting, removing, or managing inaccurate, inconsistent, incomplete, or unsuitable data.

A large text dump is not necessarily a ready-to-use LLM dataset. Self-supervised training can work without the labels and feature engineering common in traditional machine learning, but that does not make cleaning unnecessary. Cleaning and curation turn a pile of data into a dataset suited to the behavior you want. section 4.3.2

Volume alone is a poor substitute for quality. Large datasets can help a model produce convincing syntax without ensuring that its output makes sense or fits the context. Cleaning is time-consuming and sometimes subjective, but it can support a more consistent user experience. section 4.3.2

A practical preparation pass follows these steps:

  • Define a schema for the data and check whether feature distributions suit the intended outcome; normalize or scale where appropriate. section 4.3.2
  • Look for bias and anomalies. Informed verification matters; automated checks alone can miss what needs attention. section 4.3.2
  • Convert the data into a form the model can ingest, using tokenization and embeddings for LLMs. section 4.3.2

From web scrape to training data

For a web-scraped text collection, do not treat the raw files as finished simply because they contain plenty of text. An instruct schema and annotations can help clean and analyze the collection. If the goal is finetuning, consider whether its distribution of speech acts matches the task: that choice affects how context-appropriate the generated content can be. section 4.6

Treat this as a decision framework, not a complete pipeline recipe: decide what the dataset should support, inspect it accordingly, then prepare it for the model. section 4.3.2

How LLMs in Production covers it

How deep it goes
Outlines schema, distribution, bias-checking, and ingestion steps, but stops short of implementing data pipelines.
Examples
Uses large parquet or text dumps and web-scraped datasets to show why abundant unannotated text still needs curation.
What it assumes
Assumes basic familiarity with ML datasets while explaining why self-supervision reduces the need for labels and traditional feature engineering.
What you can do after
Practice/perform a high-level cleaning pass that checks task fit, bias, anomalies, and readiness for model ingestion.
How it connects
Connects cleaning to self-supervised learning, schemas, normalization, bias detection, tokenization, embeddings, and finetuning.
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