The generation of classical Chinese \textit{Ci} poetry, a form demanding a sophisticated blend of structural rigidity, rhythmic harmony, and artistic quality, poses a significant challenge for large language models (LLMs). To systematically evaluate and advance this capability, we introduce \textbf{C}hinese \textbf{Ci}pai \textbf{V}ariants (\textbf{CCiV}), a benchmark designed to assess LLM-generated \textit{Ci} poetry across these three dimensions: structure, rhythm, and quality. Our evaluation of 17 LLMs on 30 \textit{Cipai} reveals two critical phenomena: models frequently generate valid but unexpected historical variants of a poetic form, and adherence to tonal patterns is substantially harder than structural rules. We further show that form-aware prompting can improve structural and tonal control for stronger models, while potentially degrading weaker ones. Finally, we observe weak and inconsistent alignment between formal correctness and literary quality in our sample. CCiV highlights the need for variant-aware evaluation and more holistic constrained creative generation methods.

翻译：古典中文词生成要求结构严谨、韵律和谐与艺术品质的复杂融合，这对大语言模型构成了重大挑战。为系统评估并推进此能力，我们提出了**中**文**词**牌**变**体基准（**CCiV**），该基准旨在从结构、声韵与质量三个维度评估大语言模型生成的词作品。通过对17个大语言模型在30个词牌上的评估，我们揭示了两个关键现象：模型常生成有效但非预期的历史词体变体，且遵循平仄格律比结构规则更为困难。我们进一步证明，对于较强模型，形式感知提示能提升结构与声韵控制能力，但可能削弱较弱模型的表现。最后，我们发现在样本中形式正确性与文学质量间存在微弱且不一致的关联。CCiV凸显了变体感知评估与更全面的约束性创意生成方法的必要性。