Large language models often remain sensitive to answer format: a question solved correctly in one form may fail in another semantically equivalent form. To study this gap, we define cross-format robustness as the extent to which a model answers the same underlying question consistently across formats. We then compare full-format training with FormatMix, which expands only a subset of training items into multiple equivalent formats using either random or targeted selection. Across GLM4 and Llama-3.1, multi-format supervision consistently improves both task performance and cross-format robustness, whereas Multiple-choice question (MCQ)-only supervision alone brings little benefit and can even reduce robustness. We further find that expanding only about 30% of the training set into multiple formats often recovers most of the gain from full-format training, and this effect appears across the model families and sizes we study. These results suggest that format diversity, rather than additional supervision alone, is the key driver of robustness. That lightweight multi-format augmentation is a practical way to make LLMs less sensitive to answer format without changing the base model.

翻译：大型语言模型往往对答案格式依然敏感：以一种格式正确解决的问题，在语义等价的另一种格式下可能失败。为研究这一差距，我们将跨格式鲁棒性定义为模型在不同格式下一致回答同一底层问题的程度。随后，我们比较了全格式训练与FormatMix方法——后者仅通过随机或针对性选择，将部分训练样本扩展为多种等价格式。在GLM4和Llama-3.1上，多格式监督持续提升了任务性能与跨格式鲁棒性，而仅使用多项选择题（MCQ）式监督则几乎无益处，甚至可能降低鲁棒性。我们还发现，仅将约30%的训练集扩展为多种格式，往往就能恢复全格式训练的大部分收益，且这一效应在我们研究的模型系列和规模中普遍存在。这些结果表明，格式多样性（而非单纯的额外监督）是鲁棒性的关键驱动因素。轻量级的多格式增强是一种实用方法，可在不改变基础模型的情况下，使LLM对答案格式的敏感度降低。