Self-Instruct Evaluation

Self-Instruct Evaluation

The self_instruct step implements the Self-Instruct method for generating synthetic instruction-following datasets and using them for evaluation.

Overview

Self-Instruct allows for the automatic generation of instruction-tuning data by using an existing language model to create diverse instruction-response pairs. In FreeEval, this step can both generate synthetic evaluation datasets and evaluate models on them.

Key Features

• Dataset Generation: Creates synthetic instruction datasets from seed examples
• Domain Customization: Can focus on specific domains or skills
• Quality Filtering: Includes techniques to ensure high-quality instructions
• Format Flexibility: Supports multiple choice or open-ended question generation

When to Use

Use this step when you want to:

• Create custom evaluation datasets for specialized domains
• Test model performance on instructions not found in public benchmarks
• Generate additional training data for instruction tuning
• Evaluate model capabilities beyond standard benchmarks

Implementation Details

Internally, this step:

1. Starts with seed examples from an existing dataset
2. Uses a capable LLM (often GPT-4) to generate new examples
3. Creates variations and extends the dataset with new instructions
4. Can filter and validate the generated examples
5. Formats the output for evaluation or fine-tuning

Technical Considerations

For high-quality instruction generation, it’s recommended to use a strong model like GPT-4. The quality of the generated dataset highly depends on the system prompt and seed examples provided.