Instruction-following benchmarks remain predominantly English-centric, leaving a critical evaluation gap for the hundreds of millions of Indic language speakers. We introduce IndicIFEval, a benchmark evaluating constrained generation of LLMs across 14 Indic languages using automatically verifiable, rule-based instructions. It comprises around 800 human-verified examples per language spread across two complementary subsets: IndicIFEval-Ground, translated prompts from IFEval (Zhou et al., 2023) carefully localized for Indic contexts, and IndicIFEval-Ground, synthetically generated instructions grounded in native Indic content. We conduct a comprehensive evaluation of major open-weight and proprietary models spanning both reasoning and non-reasoning models. While models maintain strong adherence to formatting constraints, they struggle significantly with lexical and cross-lingual tasks -- and despite progress in high-resource languages, instruction-following across the broader Indic family lags significantly behind English. We release IndicIFEval and its evaluation scripts to support progress on multilingual constrained generation (http://github.com/ai4bharat/IndicIFEval).

翻译：指令遵循评估基准目前仍以英语为中心，导致对亿万印度语言使用者的关键评估存在空白。本文提出IndicIFEval，这是一个通过可自动验证的基于规则的指令，评估大语言模型在14种印度语言上受限生成能力的基准。该基准每种语言包含约800个人工验证的示例，分布于两个互补的子集：IndicIFEval-Ground（基于IFEval（Zhou等人，2023）提示经针对印度语境细致本地化翻译而成）与IndicIFEval-Ground（基于本土印度语内容合成生成的指令）。我们对涵盖推理与非推理模型的主要开源权重模型及专有模型进行了全面评估。结果显示，模型虽能较好地遵循格式约束，但在词汇及跨语言任务上表现显著不足——尽管高资源语言已取得进展，但整个印度语系的指令遵循能力仍大幅落后于英语。我们公开IndicIFEval及其评估脚本以支持多语言受限生成研究的进展（http://github.com/ai4bharat/IndicIFEval）。