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This paper introduces the Decomposed Requirements Following Ratio (DRFR), anew metric for evaluating Large Language Models' (LLMs) ability to followinstructions. Addressing a gap in current methodologies, DRFR breaks downcomplex instructions into simpler criteria, facilitating a detailed analysis ofLLMs' compliance with various aspects of tasks. Alongside this metric, wepresent InFoBench, a benchmark comprising 500 diverse instructions and 2,250decomposed questions across multiple constraint categories. Our experimentscompare DRFR with traditional scoring methods and explore annotation sources,including human experts, crowd-sourced workers, and GPT-4. The findingsdemonstrate DRFR's higher reliability and the effectiveness of using GPT-4 as acost-efficient annotator. The evaluation of several advanced LLMs using thisframework reveals their strengths and areas needing improvement, particularlyin complex instruction-following. This study contributes a novel metric andbenchmark, offering insights for future LLM development and evaluation.

InFoBench: Evaluating Instruction Following Ability in Large Language Models