Categoryexternal
MeasureTurn-specific accuracy average on a 0-1 scale
Tasks4,501 three-turn conversations in eight languages
Multi-IF tests instruction following over three turns in eight languages. This selected historical author report preserves each turn score, dated API identifier, and generation settings for four models.
No model in the catalog has a published Multi-IF score.The benchmark is defined by Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following, but the catalog carries no value for it yet. An absent value is shown as absent here rather than as a zero.
About Multi-IF
What the benchmark measures and how its value is published, from the source’s own record.
What is Multi-IF?
Multi-IF is an external benchmark published by Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following. Multi-IF tests instruction following over three turns in eight languages. This selected historical author report preserves each turn score, dated API identifier, and generation settings for four models. Its evaluation set is described by the source as 4,501 three-turn conversations in eight languages.
How is Multi-IF scored?
Each model's result is published by Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following as turn-specific accuracy average on a 0-1 scale. ModelScale prints that value to 3 decimal places, exactly as published, and never converts or rescales it. This page ranks the 0 models that carry a published value, highest value first; a model the source never scored is left out rather than ranked at zero. The source does not state whether a higher value is the better result, so neither does this page.
ModelScale