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FreeEval Documentation

Alpaca Eval

Alpaca Eval

The alpaca_eval step implements Alpaca Eval, a benchmark for evaluating instruction-following capabilities through pairwise comparisons.

Overview

Alpaca Eval evaluates models on instruction-following ability using a pairwise comparison methodology. Given the same instruction, responses from two different models are evaluated, typically by another LLM acting as a judge, to determine which response better fulfills the instruction.

Key Features

  • Pairwise Comparison: Direct comparison between two models’ responses
  • Instruction-Following Focus: Tests ability to follow natural language instructions
  • Win Rate Metrics: Provides clear win/loss statistics
  • LLM-as-Judge: Uses capable models to evaluate response quality

When to Use

Use this step when you want to:

  • Compare two models’ instruction-following capabilities
  • Get a relative ranking between models
  • Evaluate on diverse, general instructions
  • Test alignment with human preferences

Implementation Details

Internally, this step:

  1. Selects instructions from a curated dataset
  2. Obtains responses from two candidate models
  3. Presents both responses to a judge model
  4. Records which model’s response was preferred
  5. Calculates win rates and other comparison statistics

Technical Considerations

The quality of Alpaca Eval depends significantly on the judge model used. For consistent results comparable to published benchmarks, GPT-4 is recommended as the judge.