Improving Cross-Format Robustness in Language Models with Multi-Format Training 事件

Improving Cross-Format Robustness in Language Models with Multi-Format Training arXiv:2606.11643v1 Announce Type: new Abstract: 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 o