LyriCo: Controllable Chinese Lyric Generation with Large Language Models

Controllable Chinese lyric generation requires models to satisfy both content requirements and fine-grained structural constraints, including section layout, line count, line length, word-group lengths, and rhyme. We present LyriCo, a structured-text framework that encodes structural specifications as control tokens and fine-tunes decoder-only language models on annotated lyrics. Its data pipeline combines classifier-based genre annotation, theme labels extracted by a large language model, text-to-speech (TTS)-inspired Chinese word segmentation, and section annotations derived from music structure analysis and lyric alignment. We introduce two lightweight mechanisms to strengthen structural control: increased loss weights on control tokens and a line-local attention bias toward relevant control tokens. On a benchmark of 500 briefs spanning Pop and gufeng, ablation studies across seven configurations at the 1.5B parameter scale evaluate the contributions of both mechanisms. Compared with the standard supervised fine-tuning baseline, the combined configuration improves line-count accuracy from 93.40% to 96.80%, section accuracy from 95.19% to 98.80%, and word-format accuracy from 97.96% to 98.52%. With a 14B backbone, LyriCo achieves higher line-length, rhyme, and word-format accuracy than prompted GPT-5.5 and DeepSeek-V4-Pro on the same benchmark, while tying with GPT-5.5 for the highest line-count accuracy. These results show that emphasizing explicit control signals can improve compliance with structural constraints in Chinese lyric generation.