About IFScale
IFScale is a novel benchmark designed to evaluate large language model instruction-following performance as the number of instructions increases. Real-world applications often demand adherence to dozens or hundreds of simultaneous requirements and this is the first benchmark to measure instruction-following performance at scale. The benchmark task is to generate a business report while including a list of keywords specified in the instructions.
Our benchmark reveals 3 distinct performance degradation patterns, bias towards earlier instructions, and distinct categories of instruction-following errors under the cognitive load of increasing instructions. We evaluate 20 state-of-the-art models across seven major providers, providing critical insights for reliable deployment of LLMs in complex, multi-instruction scenarios.
Comprehensive Evaluation
Systematic evaluation revealing performance hierarchies and degradation patterns across state-of-the-art models, addressing the critical gap in understanding high-density instruction scenarios.
Multi-dimensional Analysis
Detailed exploration of standard deviation patterns, primacy effects, error types, and curve patterns for all models considered, highlighting standard patterns and outliers.
IFScale Framework
A novel benchmark designed to characterize how models perform under the cognitive load of increasing instructions, providing essential insights for reliable deployment in complex, multi-instruction scenarios.