An introduction to preparing your own dataset for LLM training

This comprehensive article by AWS experts provides an in-depth guide to preparing datasets for Large Language Model (LLM) training, covering critical aspects of data preprocessing, deduplication, and fine-tuning.

• Key data preprocessing steps include:
  • Extracting text from various file formats (HTML, PDF, Office documents)
  • Filtering low-quality content
  • Removing duplicates using techniques like MinHash
• Dataset preparation for fine-tuning involves:
  • Ensuring domain relevance
  • Maintaining high annotation quality
  • Addressing ethical considerations
• Data labeling approaches include:
  • Human labelers
  • LLM-assisted labeling
  • Cohort-based labeling
  • Reinforcement Learning from Human Feedback (RLHF)

The article emphasizes that high-quality dataset preparation is crucial for developing accurate and reliable Large Language Models.