LLM Annotation

Definition

LLM annotation refers to labeling data specifically designed for training and evaluating large language models. It includes tasks like intent recognition, entity tagging, and preference ranking.

Purpose

The purpose is to create high-quality datasets that align LLMs with human expectations. Annotation improves performance, reduces bias, and enables reinforcement learning with human feedback.

Importance

  • Provides fine-grained supervision for massive models.
  • Improves safety by curating datasets with human review.
  • Supports evaluation benchmarks for LLMs.
  • Often combined with preference annotation for fine-tuning.

How It Works

  1. Define annotation tasks for LLM (e.g., summarization, dialogue intent).
  2. Collect diverse raw text data.
  3. Annotators label tasks with instructions and categories.
  4. Aggregate results and ensure inter-annotator agreement.
  5. Use labeled data for fine-tuning or evaluation.

Examples (Real World)

  • OpenAI’s RLHF datasets: preference-labeled text for model alignment.
  • Anthropic’s Constitutional AI: annotated rules for safer responses.
  • Hugging Face datasets: community-curated text datasets for LLM tasks.

References / Further Reading