In BLOOM: Creativity and Affinity in Artificial Lyrics and Art
Abstract
We apply a range of sampling parameters to a large multilin- gual 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 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 single releases, we leverage a custom fine-tuned stable diffusion model to produce highquality lyric-guided album art, demonstrating a creative ap- proach for an artist seeking inspiration for an album or single.
Finally, we introduce the MojimLyrics dataset, a Chinese- language dataset of popular song lyrics, which allows us to identify a gap between MAUVE scores and subjective human quality assessment.
Introduction
Modern music production — which spans elements including musical composition, songwriting, album artwork and music videos — is a tremendous domain for exploring creativity across modalities.
This work focuses on better understanding subjective human assessment of creative text produced by large language models (LLM), demonstrating that mimicry of an existing sample dataset may not always be the best approach when considering human preferences.
Towards this end we use BLOOM (BigScience 2022), a multilingual LLM with 176B parameters, to generate Chinese song lyrics with varying sampling parameters.
We focus on top- p sampling, as this method has been found to outperform top- k and beam search in prior research (Holtzman et al. 2019).
In order to assess the quality of the generated lyrics, we task human reviewers to provide feedback on lyrical coherence, creativity, and enjoyment.
Using this data, we demonstrate the relationship between creativity and coherence in human-assessed lyric quality, and highlight differences between subjective human assessment and computational methods such as M AUVE (Pillutla et al. 2021).
Beyond comparison of creativity measures, we also dive into new emerging multimodal possibilities related to AIdriven creativity.
Music today is often accompanied by cover art that evokes the aesthetic of the piece.
This art, previously displayed on record covers and lyric booklets, remains prominently displayed on music apps and streaming platforms.
While album cover art may reflect a collection of songs (an album, LP, or EP), digital music publishing allows for easy release of singles, and cover art is increasingly used to accompany individual tracks (Leight 2018).
Using our lyrics, we use the Chinese-language Taiyi stable diffusion model (Wang et al. 2022) to generate an album image inspired by the LLM-generated lyrics.
The demonstrated workflow may be used for inspiration by songwriters seeking a unified lyrical and visual concept for a single or album, as well as being a source of entertainment to music fans.
Related Work
Previous work has used LLMs to generate classical Chinese poetry (Liao et al. 2019).
Work has also been performed which uses GPT-2 as a generation system for various types of creative Chinese writing, contemporary song lyrics among them (Zhang et al. 2022).
Related work has used an image-generation network as an intermediate step in generating non-lyrical creative text, based on the idea of an author “visualizing” while they work (Zhu et al. 2022).
In English, research has similarly been done on LLM lyric generation (Rodrigues et al. 2022), including lyrics conditioned on a song’s melody (Chen and Lerch 2020).
Our work is distinct from previous work in that it focuses on zero-shot lyric generation with a massive multilingual model (BLOOM), is the first to our knowledge to benchmark lyrics against evaluation metrics that strongly correlate to human assessment ( M AUVE ), and most importantly, presents an investigation into the relationship between computational assessment of open-ended text quality, and subjective measurements.
In this department, we are inspired by previous work on quantifying creativity that found that KL divergence has a strong correlation with human-assessed creativity for word pairs (Kuznetsova, Chen, and Choi 2013).
Finally, this work is also the first research work to apply diffusion models as part of a unified approach for generating coordinated cover art in association with song lyrics.
All models and code and from our work will be made available open-source to enable future research 1 .
Methodology
We organize our methodology into three major parts: 1) collecting and cleaning Chinese lyrics found online to create a novel dataset of popular Chinese lyrics for use with M AUVE , 2) performing lyric generation effectively with the BLOOM176B LLM, and 3) utilizing the Taiyi stable diffusion model to generate high-quality art to accompany the lyrics.
Dataset
For comparison to LLM-generated lyrics, we collect a dataset of song lyrics for popular Chinese songs from the website Mojim.com.
Mojim is a popular lyrics sharing website that focuses on Chinese song lyrics.
We request the top popular artists in the “male”, “female”, and “group” categories, fetch all the albums associated with each artist, and download the lyrics for every song in each album.
Metrics on the dataset can be found in Table 1.
Data downloaded from Mojim.com is in raw HTML format and must be cleaned to extract raw lyrics.
This process is summarized as follows.
• Removal of HTML tags, as well as the removal of markup symbols not typically part of the lyric text, such as “*” and “#” and various unicode equivalents.
• Removal of site-specific text characteristics, such as: a recurring line in Chinese which translates to “Find more lyrics at Mojim.com”, dividing lines composed of hyphens, non-standard labels placed at the start of the song.
• Compress instances of many subsequent newline characters into just two newline characters, to reduce excess whitespace which may emerge after removing tags, annotations, and symbol markup.
[Table]
Lyric Generation
We generate zero-shot samples using the multilingual
BLOOM-176B model (BigScience 2022) at varying values of p for top- p nucleus sampling (Holtzman et al. 2019).
By adjusting the probability mass used to sample the next token, we produce varied lyric sequences that can be used to analyze quantitative and subjective text characteristics.
Our evaluation of lyrics focuses on two major approaches:
• Computational Quality : We measure computational quality via commonly-used metrics that target language model degeneration (repeated n -grams, diversity of tokens, distinct n -grams), as well as distributional information via M AUVE (Pillutla et al. 2021).
Subjective : Subjective measures of coherence are collected via a carefully survey on Amazon Mechanical Turk.
These measures include an assessment of how creative, coherent, and enjoyable the lyrics are.
Previous research on quantifying creativity indicates that for measuring perceived creativity of word pairs { w 1 , w 2 } , the KL divergence of { w 1 , w 1 w 2 } is among the most effective measures (Kuznetsova, Chen, and Choi 2013).
M AUVE is designed to jointly capture two types of errors between the output distribution Q of a model, and the human distibution P .
1) Type I errors, where Q places high mass on text which is unlikely under P ; and, 2) Type II errors, where Q cannot generate text which is plausible under
P .
M AUVE does this by summarizing Type I and Type II errors using Kullback–Leibler (KL) divergences, which are measured softly using a mixture distribution.
Due to the similarity between M AUVE ’s computation and previous work on creativity quantification, it is possible that M AUVE may partially reflect subjective human creativity assessment, which may contribute to M AUVE ’s strong performance for evaluating
generated text
quality more generally.
Additional text quality metrics for assessing language model output are selected based on previous work (Zhu et al. 2022;
Su et al. 2022).
These metrics include rep- n = 1.0 - | unique n -grams | | total n -grams | measuring duplicate n -grams within each generated sequence (Welleck et al. 2020), distinct- n = | unique n -grams | | length of text | (Li et al. 2016), and diversity =
(cid:81) 4 n =2 (1 − rep- n ) for measuring diversity of n -grams (Su et al. 2022).
Autoregressive language models such as BLOOM perform sampling to select a next token typically using some variation of top- k , beam search, and nucleus top-
p sampling.
Due to the strong documented performance of top- p sampling (Holtzman et al. 2019), we produce BLOOM generations at varying levels of p ∈ [0 . 80 , 0 . 85 , 0 . 90 , 0 .
We leverage our generated song lyrics to create accompanying track art inspired by the song lyrics.
As a generative model, we utilize the Chinese-language Taiyi Stable
Diffusion model (Wang et al.
Rombach et al. 2021), which includes a Chinese-language text encoder.
We use this model to produce 1000 samples of album artwork, from seeds 0-999, using the prompt “album art” in Chinese, followed by our generated lyrics.
We perform this at varying settings of classifier-free diffusion guidance (CFG), where
CFG ∈ [4 . 0 , 7 . 0 , 10 . 0] .
Images are generated with 20 sampling steps using the Euler A method (Rombach et al. 2021).
For evaluation, we utilize a dataset of approximately 4, album covers at 512x512 resolution (Surma 2019).
We determine the quality of the generated images by calculating
Fr´echet Inception Distance (FID) (Heusel et al. 2017; Seitzer 2020) using the pool 3 activations from an Inception network (Szegedy et al. 2015) between generated album covers and the real album dataset.
Experimental Setup
Inference on BLOOM-176B was performed via the Huggingface Inference API.
All other calculations (stable diffusion, M AUVE , FID) were performed on a machine with an Intel i7-6800K 12-core CPU, 32GB RAM, and a 24GB
VRAM RTX 3090 GPU.
Seeds starting at 0 are used throughout our experiments for reproducibility.
BLOOM
In order to use BLOOM to generate lyrics in a zero-shot setting, we provide the following prompt:
[Figure]
This prompt was chosen based on the observed result of Chinese language song lyrics.
Shorter prompts such as simply the words for “lyrics” in Chinese more often resulted in non-song lyrics such as interviews with musicians,
Source rep-2 ↓ rep-3 ↓ rep-4 ↓ diversity
[Figure]
[Table]
-based quality results
176B
- ated text at varying sampling probability mass p and more specific prompts that mentioned specific artists or too closely resembled formatting from specific lyric websites had a higher incidence of generating lyric sequences from real songs.
Memorization in large language models is well-documented and is a significant problem undergoing research (Carlini et al. 2021).
The selected prompt appears to minimize this behaviour, though future analysis on memorization would be valuable.
Amazon Mechanical Turk
Using Amazon Mechanical Turk to evaluate open-ended text generation is challenging and requires care to perform effectively (Karpinska, Akoury, and Iyyer 2021), however, such platforms remain useful for their ability to provide real human feedback, A selection of 60 samples were taken from the BLOOM generated lyric corpora at each value for top − p nucleus sampling, as well as lyrics from the MojimLyrics dataset.
For each of these 60 samples, 3 different reviewers were assigned to score the lyrics for coherence, creativity, affinity (how much the reviewer likes the lyrics), and recognition (whether they believe they may have seen the lyrics before).
Answers for each question are converted into normalized scores between 0 and 10.
Instructions were provided in Simplified Chinese.
To encourage close reading, a starting question requests a short summary of the lyrics, and a final multiple-choice question tests the reviewer’s basic fluency in Chinese by placing the option “I can read Chinese” among various other negative options. Participant responses were removed if they failed to provide a summary or respond correctly to the literacytesting question.
The projected task duration was 120 seconds, with payment of $0.44 CAD for a projected hourly wage of $13. CAD.
Actual mean task duration was confirmed to be within the projected task duration.
The survey questions are provided in the GitHub repository.
MAUVE Calculation
For calculating M AUVE , we featurize p with a random sample of 3000 real lyrics from the MojimLyrics dataset, and featurize q with 3000 generated lyrics from BLOOM-176B at each probability mass from our nucleus sampling p ∈
We set a maximum text length
[Table]
Results
Results for language model evaluation with M AUVE scores and degeneration metrics can be found in Table 2, while results of stable diffusion CFG variation on FID can be found in Table 3.
The complete dataset of generated BLOOM lyrics are provided in the GitHub repository.
Human assessment of lyrics can be found in Figure 3.
A sample of generated lyrics and the resulting stable diffusion art can be found in Figure 1.
A wider range of stable diffusion images using the album art prompt can be found in
Figure 2, demonstrating the variety of generations the model can produce.
FID scores calculated between outputs of the diffusion model and real album art can be found in Table 3.
On the 10 point normalized scale, mean standard deviation among multiple responses for the same input sample were observed to be 1.29, 1.68, 1.95, and 2.67 for coherence, affinity, creativity, and recognition respectively.
However, despite the relatively low variance in response scores, measures of inter-annotator agreement (IAA) are consistently low (i.e., negative Krippendorff ordinal alpha). Survey responses were highly skewed towards positive answers. When a reviewer gave a particular sample a low score, often it was only one reviewer did so. This implies that while reviewers largely agreed on ratings, there was not a consistent criterion across reviewers for giving low scores.
Obtaining high IAA scores on concepts such as creativity and personal preference will likely require a larger study with a greater number of trained reviewers.
Discussion
Sampling BLOOM for lyrics from a greater portion of the probability mass tends to improve quantitative metrics of generated lyric quality, as shown in Table 2.
As expected, token diversity, distinct- n , and rep- n all improve at increasing p portion of the probability mass.
That M AUVE score also continues improving implies a low incidence of Type
I errors where the model output distribution Q places high probability mass on text which is unlikely under P .
Notably, even at very high p -values, BLOOM-176B generated lyrics are still more repetitive and less diverse than human lyrics.
Human assessment of lyrics at varying p is charted in Figure 3.
Coherence is highest at low- p , where common words are frequently sampled, but with a low accompanying creativity score.
Subjective quality measurements of creativity and affinity peak around p = 0 .
9 , and trend downwards as the model selected less common words and became less coherent.
This implies that for a human to recognize work as “creative”, it must be coherent enough to be understood, but distinctive enough to be interesting.
While M AUVE correlates with creativity as it increases from p = 0 .
80 to p = 0 .
95 , this inverts at higher p sampling.
Finally, it is notable that real popular lyrics uploaded to
Mojim.com are at points deemed less creative, coherent, and likable than BLOOM-176B generations.
Resemblance to a human dataset does not inherently bestow positive perceived qualities to generated art.
While the best-scoring CFG setting in Table 3 appears to be at CFG=4.0, this measurement alone is not indicative of subjective human quality.
Conclusion
In conclusion, we have demonstrated the limits of current computational techniques for assessing subjective qualities of song lyrics.
Based on human reviewers, we find that large language models such as BLOOM-176B can be used to produce zero-shot song lyrics that are evaluated favourably in comparison to real song lyrics.
Inpsired by AI-driven creativity, we provide an approach for generating album art based on lyrics using diffusion.
Finally, we provide a novel dataset of in-the-wild popular Chinese song lyrics, and the code for repeating our experiments.
Future Work
We identify the following open problems for future research.
• Quantification or representation of abstract concepts such as creativity, and shaping generative models to produce outputs that manifest such attributes, across modalities.
• Analysis of sampling parameters and prompts on characteristics of LLM text generation, incorporating both M AUVE and subjective human assessment (including a more comprehensive subjective study).
• Deeper study of large multilingual language models such as BLOOM-176B, on topics such as training data memorization, overparameterization, and fairness.
Acknowledgments
Thank you to Annie Feng for survey proofreading, to Bowen
Gu for past discussions on lyric generation and workers at
Amazon Mechanical Turk for participating in this study