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AI Evaluation & Observability

AlpacaEval

AlpacaEval is an open-source tool for automatically comparing instruction-following language models. It estimates preference by asking an evaluator model to compare candidate outputs with reference outputs, then reports win rates and leaderboard results.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

AlpacaEval — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use AlpacaEval when you need a quick, repeatable preference-based comparison for simple instruction-following tasks, especially during model development.

Who it is for

  • Language-model researchers comparing instruction-following systems.
  • Developers who need repeatable evaluation signals during model development.

Documented uses

  • Evaluate candidate-model outputs against reference outputs.
  • Generate a leaderboard from evaluation results.
  • Run repeated automatic comparisons as part of model development.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 code license. The documented CLI workflow uses Python 3.10 or later and an OpenAI API key.

  • The quick-start expects JSON entries with instruction and output fields.
  • Evaluation compares candidate outputs with a reference model; AlpacaEval 2.0 documents GPT-4 Turbo as its default reference.
  • The project reports length-controlled win rates by default and also exposes raw win rates.
  • The README cautions that evaluator preferences may favor longer outputs or style, and that the benchmark does not assess model risks.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Evaluate existing model outputs with the CLI

Generate an AlpacaEval leaderboard and annotations from an existing outputs file.

Before you start

  • Python 3.10 or later.
  • AlpacaEval installed with pip install alpaca-eval.
  • A JSON file whose entries contain instruction and output keys.
  • An OpenAI API key for the documented default evaluator.

Steps

  1. Install AlpacaEval with the command below.

    Documented command
    pip install alpaca-eval

  2. Set the OPENAI_API_KEY environment variable.

  3. Run alpaca_eval --model_outputs 'example/outputs.json', replacing the path with your outputs file.

Expected outcome

The CLI prints a leaderboard and saves the leaderboard and annotations in the same directory as the model outputs file.

Guide limitations

  • The documented default reference outputs are from GPT-4 Turbo for AlpacaEval 2.0.
  • Automated preferences can favor longer outputs or style over factuality and do not measure model risks; use the score as a development proxy, not as a replacement for human evaluation in high-stakes decisions.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
tatsu-lab/alpaca_eval · Official homepage title · retained in its source language
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