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How to generate clean data for Large Language Models (LLMs)

Updated: Jan 25

Generating clean data for training Large Language Models (LLMs) is a critical step to ensure high-quality results. The data preparation process involves gathering, preprocessing, and curating massive datasets while addressing challenges like noise, redundancy, and biases.



Data generation for LLMs
Data generation for LLMs


Below are the detailed steps:


1. Data Collection

The first step is gathering diverse and extensive datasets from various sources:

  • Sources:

    • Public Repositories: Wikipedia, Common Crawl, GitHub, Open Web Data.

    • Books: Public domain books (e.g., Project Gutenberg).

    • Research Papers: ArXiv, PubMed.

    • Domain-Specific Data: Industry-specific text like legal documents, medical records, or financial reports.

    • Multilingual Sources: For models requiring cross-lingual capabilities.

  • Considerations:

    • Ensure compliance with data usage policies (e.g., copyright, privacy laws).

    • Target diverse domains to maximize generalization.


2. Data Deduplication

Removing duplicate content to avoid redundant training and overfitting:

  • Methods:

    • Exact Matching: Identify and remove identical sequences or files.

    • Near-Duplicate Matching: Use techniques like MinHash or Locality Sensitive Hashing (LSH) to detect similar content.

  • Tools:

    • Deduplication Libraries: Python-based tools like simhash or custom scripts.

    • Cloud Services: Tools like BigQuery for large-scale deduplication.


3. Language Filtering

Identify and retain content in the desired languages:

  • Methods:

    • Use a language identification tool, such as langdetect or fastText.

    • Discard non-target language content or noisy multilingual data.

  • Common Issues:

    • Mixed-language documents.

    • Incorrect language tagging.


4. Content Quality Filtering

Remove low-quality or irrelevant data to improve training efficiency:

  • Indicators of Poor Quality:

    • Spam-like or repetitive content.

    • Content with excessive grammatical errors or incomplete sentences.

    • Short or overly simplistic documents.

  • Automated Filtering:

    • N-gram analysis to detect low-information sequences.

    • Heuristic-based rules (e.g., threshold on the number of words or punctuation density).


5. Redundancy Filtering

Avoid overrepresentation of common topics or domains to ensure balance:

  • Techniques:

    • Sampling proportional to dataset diversity.

    • Thresholding the number of similar documents from a specific domain or source.

  • Tools:

    • Topic modeling (e.g., Latent Dirichlet Allocation) to identify overrepresented domains.


6. Data Normalization

Standardize the text format to ensure uniformity:

  • Steps:

    • Convert text to lowercase (if case sensitivity is not needed).

    • Normalize Unicode characters.

    • Standardize quotes, dashes, and apostrophes.

  • Libraries:

    • Python’s unicodedata and re libraries for normalization.


7. Removing Sensitive or Offensive Content

Address privacy, ethical, and regulatory concerns:

  • Sensitive Information:

    • Personally Identifiable Information (PII): Names, addresses, phone numbers, etc.

    • Financial or medical data, unless explicitly allowed.

  • Offensive or Toxic Content:

    • Detect using toxicity classifiers (e.g., Perspective API, OpenAI’s filters).

    • Use word lists or pre-trained models to flag harmful content.


8. Tokenization

Prepare data for model training by converting text into tokens:

  • Tokenization Methods:

    • Byte Pair Encoding (BPE).

    • SentencePiece.

    • WordPiece.

  • Tools:

    • Hugging Face tokenizers.

    • sentencepiece for subword tokenization.


9. Removing Boilerplate and Formatting Artifacts

Clean extraneous content introduced by web scraping or text formatting:

  • Examples of Artifacts:

    • HTML tags and metadata.

    • Advertisement banners or footers.

    • Code comments in scraped repositories.

  • Tools:

    • Use regular expressions or libraries like BeautifulSoup for HTML parsing.


10. Data Augmentation (Optional)

If needed, augment data to improve model robustness:

  • Examples:

    • Backtranslation for creating diverse paraphrases.

    • Synonym replacement using thesaurus-based tools.

  • Considerations:

    • Balance augmentation to avoid introducing noise.


11. Bias Mitigation

Identify and address biases in the dataset:

  • Types of Bias:

    • Demographic biases (e.g., gender, race, or geographic bias).

    • Overrepresentation of specific perspectives or ideologies.

  • Mitigation Techniques:

    • Balance data representation across demographics.

    • Perform ethical audits of the dataset.


12. Dataset Structuring and Storage

Prepare the data for efficient access during training:

  • Organization:

    • Store data in chunks or shards for parallel access.

    • Save preprocessed data in formats like .txt, .jsonl, or .tfrecord.

  • Compression:

    • Use Gzip or similar tools to compress large datasets.


13. Data Validation

Ensure the cleaned dataset meets quality standards:

  • Metrics to Validate:

    • Distribution of token lengths.

    • Proportion of high-quality sentences.

    • Domain balance and language diversity.

  • Manual Spot-Checking:

    • Randomly sample documents for manual inspection.


14. Data Documentation

Document the dataset for reproducibility and transparency:

  • Details to Include:

    • Sources of data.

    • Preprocessing steps.

    • Known biases or limitations.


Summary Workflow

  1. Collect diverse datasets.

  2. Deduplicate, filter, and normalize data.

  3. Remove sensitive and offensive content.

  4. Tokenize and preprocess for model training.

  5. Validate and document the dataset.


By following these steps, you can generate clean, high-quality datasets that contribute to building robust and ethical LLMs.

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