We apply a large multilingual language model (BLOOM-176B) in open-ended
generation of Chinese song lyrics, and evaluate the resulting lyrics for
coherence and creativity using human reviewers. We find that current
computational metrics for evaluating large language model outputs (MAUVE) have
limitations in evaluation of creative writing. We note that the human concept
of creativity requires lyrics to be both comprehensible and distinctive -- and
that humans assess certain types of machine-generated lyrics to score more
highly than real lyrics by popular artists. Inspired by the inherently
multimodal nature of album releases, we leverage a Chinese-language stable
diffusion model to produce high-quality lyric-guided album art, demonstrating a
creative approach for an artist seeking inspiration for an album or single.
Finally, we introduce the MojimLyrics dataset, a Chinese-language dataset of
popular song lyrics for future research.

本研究利用大型多语言语言模型（BLOOM-176B）生成中文歌词，并使用人工评审员对结果进行了连贯性和创造性的评估，发现现有计算度量方法（MAUVE）在评估创造性写作方面存在局限性。本文通过使用中文稳定扩散模型产生高质量歌词指导的唱片封面设计，为寻找灵感的艺术家提供了一种创意方法，并介绍了 MojimLyrics 数据集，该数据集是一个用于未来研究的中文热门歌曲歌词数据集。

在 BLOOM 中：人工创作歌词和艺术中的创造力和亲和力

In BLOOM: Creativity and Affinity in Artificial Lyrics and Art

In this paper, we comprehensively study on context-aware generation of
Chinese song lyrics. Conventional text generative models generate a sequence or
sentence word by word, failing to consider the contextual relationship between
sentences. Taking account into the characteristics of lyrics, a hierarchical
attention based Seq2Seq (Sequence-to-Sequence) model is proposed for Chinese
lyrics generation. With encoding of word-level and sentence-level contextual
information, this model promotes the topic relevance and consistency of
generation. A large Chinese lyrics corpus is also leveraged for model training.
Eventually, results of automatic and human evaluations demonstrate that our
model is able to compose complete Chinese lyrics with one united topic
constraint.

本论文旨在全面研究上下文感知的中文歌词生成。提出了基于分层注意力的 Seq2Seq 模型，该模型编码了单词级和句子级上下文信息，促进了生成的主题相关性和一致性，利用大型中文歌词语料库进行模型训练，结果表明，我们的模型能够通过一致性约束生成完整的中文歌词。