ModelRefs / AlpacaEval 2 LC Methodology and Evidence
AlpacaEval 2 LC Methodology and Evidence
Length-controlled win-rate vs GPT-4 Turbo on 805 instruction-following prompts. Methodology, dataset provenance, and citation sources for AlpacaEval 2 LC.
Overview
Length-controlled win-rate vs GPT-4 Turbo on 805 instruction-following prompts.
How it is measured: LC win-rate: GPT-4 Turbo as judge; length-controlled to reduce verbosity bias.
How this benchmark is scored
| Category | open-source |
|---|---|
| Maximum score | 100 % LC win |
| Direction | Higher is better |
Primary source: https://tatsu-lab.github.io/alpaca_eval/
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Frequently asked questions
What does the AlpacaEval 2 LC benchmark measure?
Length-controlled win-rate vs GPT-4 Turbo on 805 instruction-following prompts.
What is the methodology?
LC win-rate: GPT-4 Turbo as judge; length-controlled to reduce verbosity bias.