arxiv: 2604.18489 · v1 · submitted 2026-04-20 · 💻 cs.SD · cs.CL· eess.AS

Recognition: unknown

Aligning Language Models for Lyric-to-Melody Generation with Rule-Based Musical Constraints

Hao Meng, Qiangqiang Wang, Shuran Zhou, Siyuan Zheng, Yang Song

Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:05 UTC · model grok-4.3

classification 💻 cs.SD cs.CLeess.AS

keywords lyric-to-melody generationmusic generationmodel alignmentpreference optimizationmusical constraintslanguage modelssinging melodyrule-based evaluation

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The pith

Rule-based musical constraints can align language models to generate more musically valid melodies from lyrics without human annotations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tries to solve the issue of language models producing implausible melodies when turning lyrics into tunes, such as bad rhythms or vocal ranges that do not fit singing. It establishes that musical rules can be used to automatically create preference data from the model's own generations, allowing alignment training that favors better outputs. This is done through a two-step process of preference optimization on good-bad pairs and then on negative examples. If true, this would mean more practical AI tools for creating singable songs that require less expert oversight. Readers would care because it improves the quality and usability of automatic music composition from text.

Core claim

The authors show that defining rule-based musical constraints allows automatic generation of a preference dataset from supervised fine-tuned model outputs, which can then be used to align the model first via direct preference optimization on paired data and subsequently via Kahneman-Tversky optimization on unpaired negative samples, resulting in substantially reduced rule violations and improved musicality and coherence in generated melodies according to objective and subjective evaluations.

What carries the argument

Rule-based musical constraints that automatically label preference data for sequential alignment of the language model.

Load-bearing premise

That the predefined musical rules correctly capture what makes a melody musically plausible and that optimizing the model to avoid violating them leads to outputs that are actually more musical rather than just more rule-following.

What would settle it

If in blind listening tests, participants rate the melodies from the original supervised fine-tuned model as more musical or preferable to those from the aligned model despite the latter having fewer rule violations.

read the original abstract

Large Language Models (LLMs) show promise in lyric-to-melody generation, but models trained with Supervised Fine-Tuning (SFT) often produce musically implausible melodies with issues like poor rhythm and unsuitable vocal ranges, a phenomenon we term "constraint violation". To address this, we propose a novel alignment framework that instills musical knowledge without human annotation. We define rule-based musical constraints to automatically generate a preference dataset from an SFT model's outputs. The model is then aligned through a sequential process, first using Direct Preference Optimization (DPO) on paired preference data, followed by Kahneman-Tversky Optimization (KTO) on unpaired negative samples. Experimental results demonstrate that our aligned model substantially reduces rule violations and outperforms strong baselines in both objective and subjective evaluations, generating melodies with substantially improved musicality and coherence. An interactive demo with audio comparisons is available at https://arain233.github.io/AligningMelody-demo.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

read the letter

The paper chains DPO on rule-derived paired preferences with KTO on unpaired negatives to cut rule violations in lyric-to-melody generation and reports better objective plus subjective scores, but the hand rules may mainly teach compliance rather than broader musical improvement. They start from an SFT model, apply fixed constraints on rhythm, vocal range and similar properties to label its outputs automatically, then run the two-stage alignment. The result is fewer violations and gains on both metrics and listening tests versus baselines. This is a clean, annotation-free way to inject domain knowledge into the model for this narrow task. The sequential DPO-then-KTO pipeline plus the specific music rules form a new combination not covered in the cited prior work, and the automatic labeling step is a practical plus when human musical judgments are costly. The experiments appear to show consistent drops in the targeted problems along with improved coherence ratings. The soft spot is that the rules serve as a proxy for quality. If they are incomplete or stylistically narrow, the optimization can reward outputs that simply dodge the listed issues while staying limited or formulaic. Subjective tests could then reflect rule adherence more than independent musical judgment, and the paper would be stronger with an external check such as correlation to human-composed references or rule-blind listening. Details on baseline models, exact metrics, and statistical tests are needed to fully assess the claims. This is mainly for researchers working on LLM-based music generation who want alignment methods that avoid crowdsourcing. It deserves peer review because the method is clear, the results are reported on both objective and subjective axes, and the core engineering contribution holds up even if the interpretation of musicality gains needs more scrutiny.

Referee Report

3 major / 0 minor

Summary. The manuscript proposes a rule-based alignment framework for LLMs performing lyric-to-melody generation. It identifies constraint violations (poor rhythm, unsuitable vocal ranges) in SFT outputs and defines hand-crafted musical rules to automatically generate preference data from those outputs. The model is then aligned first with DPO on the resulting pairs and subsequently with KTO on unpaired negative samples. The central claim is that the resulting model substantially reduces rule violations while outperforming strong baselines on both objective and subjective metrics, producing melodies with improved musicality and coherence. An interactive demo with audio examples is referenced.

Significance. If the experimental claims hold after fuller validation, the work would provide a scalable, annotation-free method for injecting domain-specific musical constraints into generative models. The sequential DPO-then-KTO pipeline combined with automatic rule-based preference construction is a practical contribution for controllable creative generation tasks. The absence of human preference collection and the availability of an audio demo are clear strengths that lower barriers to reproducibility and extension.

major comments (3)

[Abstract] Abstract: the claim that the aligned model 'substantially reduces rule violations and outperforms strong baselines in both objective and subjective evaluations' is presented without naming the baseline models, specifying the objective metrics (e.g., exact violation counts or melody quality scores), describing the subjective protocol (rater count, criteria, blinding), or reporting statistical tests. These omissions make the central performance claim impossible to assess.
[Method (preference data generation)] Preference data generation (method): preference pairs are created by applying the same hand-defined rules (rhythm, vocal range, etc.) to SFT outputs. No independent validation is described that would show reduced violations correlate with human judgments of musical quality on professionally composed references or on rule-independent listening tests. This leaves open the possibility that optimization rewards rule compliance rather than broader musical plausibility.
[Experimental results] Experimental results: the manuscript does not discuss controls for data leakage or overlap between the rule application used to create training preferences and the rules used to compute evaluation violation rates. If evaluation re-uses the identical rule set without hold-out or external anchors, reported gains may not reflect genuine generalization.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback on our manuscript. We address each major comment point by point below, with clear indications of planned revisions where appropriate.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that the aligned model 'substantially reduces rule violations and outperforms strong baselines in both objective and subjective evaluations' is presented without naming the baseline models, specifying the objective metrics (e.g., exact violation counts or melody quality scores), describing the subjective protocol (rater count, criteria, blinding), or reporting statistical tests. These omissions make the central performance claim impossible to assess.

Authors: We agree that the abstract would benefit from greater specificity to allow readers to evaluate the claims more readily. In the revised version, we will expand the abstract to name the baseline models (SFT and the other compared approaches), specify the objective metrics (violation counts for rhythm and vocal range, plus coherence scores), describe the subjective protocol (rater count, criteria such as musicality and coherence, blinding procedure), and note the statistical tests used to assess significance. revision: yes
Referee: [Method (preference data generation)] Preference data generation (method): preference pairs are created by applying the same hand-defined rules (rhythm, vocal range, etc.) to SFT outputs. No independent validation is described that would show reduced violations correlate with human judgments of musical quality on professionally composed references or on rule-independent listening tests. This leaves open the possibility that optimization rewards rule compliance rather than broader musical plausibility.

Authors: We acknowledge the value of an explicit correlation analysis between rule-violation reductions and human judgments on professional references or rule-independent tests. Our existing subjective evaluations demonstrate listener preference for the aligned model's musicality and coherence, but we did not conduct a dedicated correlation study of this form. We will revise the manuscript to add a discussion of this point in the experiments or limitations section, framing the rules as established musical principles while noting the absence of such targeted validation as a limitation. revision: partial
Referee: [Experimental results] Experimental results: the manuscript does not discuss controls for data leakage or overlap between the rule application used to create training preferences and the rules used to compute evaluation violation rates. If evaluation re-uses the identical rule set without hold-out or external anchors, reported gains may not reflect genuine generalization.

Authors: We will add a clarifying paragraph in the experimental setup and results sections stating that the same fixed rule set is used by design for both preference generation and evaluation, as the alignment objective is precisely to reduce violations of these constraints. The test lyrics are held out from training data. We will also discuss that this measures targeted constraint adherence rather than generalization to entirely new rules, and note the implications for interpreting the reported gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper defines external rule-based musical constraints to generate preference pairs from SFT outputs, then applies DPO and KTO; objective reductions in rule violations are shown via comparisons to baselines rather than by construction alone, while subjective evaluations supply an independent anchor for musicality claims. No self-definitional reductions, fitted inputs renamed as predictions, or load-bearing self-citations appear in the chain. The derivation remains self-contained with external content from the hand-defined rules and human judgments.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the assumption that musical plausibility can be captured by a finite set of explicit, automatically checkable rules and that preference optimization will internalize these rules into the model's generation distribution.

axioms (1)

domain assumption Rule-based musical constraints can be defined to automatically evaluate melody quality without human input.
Used to generate the preference dataset from SFT model outputs.

pith-pipeline@v0.9.0 · 5476 in / 1245 out tokens · 37861 ms · 2026-05-10T03:05:13.512635+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

33 extracted references · 10 canonical work pages · 4 internal anchors

[1]

INTRODUCTION The advent of Large Language Models (LLMs) has catalyzed a paradigm shift across numerous domains of artificial intelligence, from natural language understanding to complex reasoning [1]. In the realm of creative arts, LLMs are increasingly being leveraged for generative tasks, spanning from large-scale raw audio generation [2] to the composi...
[2]

and SongGLM [4] have demonstrated that by fine-tuning a pre- trained LLM on lyric-melody pairs, it can learn to generate coherent melodies in an end-to-end, autoregressive manner. Despite these advances, the standard Supervised Fine-Tuning (SFT) paradigm has a critical limitation: it learns to imitate the sta- tistical patterns in the training data but la...
[3]

Aligning Language Models for Lyric-to-Melody Generation with Rule-Based Musical Constraints

has become a standard alignment technique. However, RLHF is notoriously complex and computationally expensive, and its re- liance on large-scale human annotation creates a significant bottle- neck [14]. This work posits that for domains where basic symbolic- level principles–such as constraints on pitch range, note duration, and vocal register–are well-de...

work page internal anchor Pith review Pith/arXiv arXiv 2026
[4]

Framework Overview Our proposed framework aligns a pretrained LLM for high-quality lyric-to-melody generation through a three-stage process, as illus- trated in Figure 1

METHOD 2.1. Framework Overview Our proposed framework aligns a pretrained LLM for high-quality lyric-to-melody generation through a three-stage process, as illus- trated in Figure 1
[5]

This initial stage equips the model with the fundamental ability to map lyrical input to a melodic output in the specified symbolic for- mat

Supervised Fine-Tuning (SFT):We begin with a pretrained LLM and fine-tune it on a large corpus of paired lyric-melody data. This initial stage equips the model with the fundamental ability to map lyrical input to a melodic output in the specified symbolic for- mat
[6]

Each generated melody is evaluated against our set of rule-based musical constraints

Preference Data Generation:The SFT model is then used to generate multiple melody candidates for a large, diverse set of unseen lyrics. Each generated melody is evaluated against our set of rule-based musical constraints. Based on this evaluation, we automatically construct a preference dataset containing both paired data (a rule-compliant ”winner” and a ...
[7]

Sequential Alignment:Finally, we perform a post-training alignment phase on the SFT model. We employ a sequential opti- mization strategy that first refines the model with DPO [15] on the paired data and then further tunes it with KTO [16] on the unpaired negative samples. This process fine-tunes the model to prefer musi- cally plausible outputs, resultin...
[8]

The output must be correctly parsable into a sequence of ‘(lyric, pitch, duration)‘ tuples

Format Constraint:This is a fundamental syntactic check to ensure the model’s output adheres to the defined symbolic repre- sentation. The output must be correctly parsable into a sequence of ‘(lyric, pitch, duration)‘ tuples
[9]

LetL in = (w 1, w2,

Lyric Constraint:The generated melody must accurately correspond to the input lyrics. LetL in = (w 1, w2, . . . , wm)be the sequence of words in the input lyric. LetL out be the sequence of non-melisma lyric tokens extracted from the generated output. This constraint requires thatL out is a valid segmentation ofL in
[10]

Let P= (p 1, p2,

Note Constraint (Monotony A voidance):To prevent musically uninteresting melodies dominated by a single pitch, we constrain the amount of consecutive note repetition. Let P= (p 1, p2, . . . , pn)be the sequence of pitches in the gener- ated melody. The constraint is satisfied if the ratio of consecutive identical pitches does not exceed a thresholdτ note:...
[11]

It comprises two conditions: •Note Length:Each note durationd i must fall within a per- ceptually valid range:d min ≤d i ≤d max

Duration Constraint (Rhythmic Plausibility):This rule en- sures that note durations are rhythmically sensible and performable. It comprises two conditions: •Note Length:Each note durationd i must fall within a per- ceptually valid range:d min ≤d i ≤d max. This prevents notes from being too short to be heard or unnaturally long. •Final Note Length:The fina...
[12]

LetPbe the pitch sequence

Register Constraint (V ocal Range):To ensure the generated melody is singable by an average person, all pitches must lie within a typical human vocal range. LetPbe the pitch sequence. The constraint requires: pmin ≤p i ≤p max,∀p i ∈P,(2) where[p min, pmax]is a predefined MIDI note range (e.g., C4 to C6). 2.4. Sequential Alignment with DPO and KTO Our alig...
[13]

EXPERIMENT 3.1. Experimental Setup Datasets:Our training data for the SFT stage consists of approxi- mately 800k Chinese and 500k English sentence-level lyric-melody pairs, aggregated from the SongComposer dataset and proprietary sources. For evaluation, we curated a test set of 1000 sentences (500 Chinese, 500 English) from the GTSinger dataset, ensuring...
[14]

Our proposed model achieves a MOS of 3.42, significantly surpass- ing all baseline methods

This evaluation, which directly assesses perceptual quality, pro- vides the most compelling evidence of our method’s effectiveness. Our proposed model achieves a MOS of 3.42, significantly surpass- ing all baseline methods. Notably, this score is very close to the ground truth (GT) audio, which received a score of 3.50, indicating that the melodies genera...
[15]

We introduced a novel alignment framework that uses codified musical constraints to auto-generate preference data for a sequential DPO-KTO process

CONCLUSION In this paper, we addressed the critical challenge of musical plau- sibility in LLM-based lyric-to-melody generation. We introduced a novel alignment framework that uses codified musical constraints to auto-generate preference data for a sequential DPO-KTO process. Our approach instills musical domain knowledge into the LLM, sub- stantially red...
[16]

Lan- guage models are few-shot learners,

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Sub- biah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al., “Lan- guage models are few-shot learners,”Advances in neural in- formation processing systems, vol. 33, pp. 1877–1901, 2020

1901
[17]

Vladimir Gligorijevi´c, P

Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever, “Jukebox: A genera- tive model for music,”arXiv preprint arXiv:2005.00341, 2020

work page arXiv 2005
[18]

Songcom- poser: A large language model for lyric and melody compo- sition in song generation,

Shuangrui Ding, Zihan Liu, Xiaowen Dong, Pan Zhang, Rui Qian, Conghui He, Dahua Lin, and Jiaqi Wang, “Songcom- poser: A large language model for lyric and melody compo- sition in song generation,”arXiv preprint arXiv:2402.17645, 2024

work page arXiv 2024
[19]

Songglm: Lyric-to-melody generation with 2d alignment encoding and multi-task pre- training,

Jiaxing Yu, Xinda Wu, Yunfei Xu, Tieyao Zhang, Songruoyao Wu, Le Ma, and Kejun Zhang, “Songglm: Lyric-to-melody generation with 2d alignment encoding and multi-task pre- training,”arXiv preprint arXiv:2402.18107, 2024

work page arXiv 2024
[20]

Step-audio-aqaa: a fully end-to- end expressive large audio language model,

Ailin Huang, Bingxin Li, Bruce Wang, Boyong Wu, Chao Yan, Chengli Feng, Heng Wang, Hongyu Zhou, Hongyuan Wang, Jingbei Li, et al., “Step-audio-aqaa: a fully end-to- end expressive large audio language model,”arXiv preprint arXiv:2506.08967, 2025

work page arXiv 2025
[21]

Glm-4-voice: Towards intelligent and human-like end-to-end spoken chatbot.arXiv preprint arXiv:2412.02612, 2024

Aohan Zeng, Zhengxiao Du, Mingdao Liu, Kedong Wang, Shengmin Jiang, Lei Zhao, Yuxiao Dong, and Jie Tang, “Glm- 4-voice: Towards intelligent and human-like end-to-end spo- ken chatbot,”arXiv preprint arXiv:2412.02612, 2024

work page arXiv 2024
[22]

Seed-music: A unified frame- work for high quality and controlled music generation,

Ye Bai, Haonan Chen, Jitong Chen, Zhuo Chen, Yi Deng, Xiaohong Dong, Lamtharn Hantrakul, Weituo Hao, Qingqing Huang, Zhongyi Huang, et al., “Seed-music: A unified frame- work for high quality and controlled music generation,”arXiv preprint arXiv:2409.09214, 2024

work page arXiv 2024
[23]

Survey of hallucination in natural language genera- tion,

Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung, “Survey of hallucination in natural language genera- tion,”ACM computing surveys, vol. 55, no. 12, pp. 1–38, 2023

2023
[24]

Songmass: Automatic song writ- ing with pre-training and alignment constraint,

Zhonghao Sheng, Kaitao Song, Xu Tan, Yi Ren, Wei Ye, Shikun Zhang, and Tao Qin, “Songmass: Automatic song writ- ing with pre-training and alignment constraint,” inProceed- ings of the AAAI Conference on Artificial Intelligence, 2021, vol. 35, pp. 13798–13805

2021
[25]

Telemelody: Lyric-to-melody generation with a template-based two-stage method,

Zeqian Ju, Peiling Lu, Xu Tan, Rui Wang, Chen Zhang, Songruoyao Wu, Kejun Zhang, Xiang-Yang Li, Tao Qin, and Tie-Yan Liu, “Telemelody: Lyric-to-melody generation with a template-based two-stage method,” inProceedings of the 2022 Conference on Empirical Methods in Natural Language Pro- cessing, 2022, pp. 5426–5437

2022
[26]

Relyme: Improving lyric-to-melody generation by incorporating lyric-melody re- lationships,

Chen Zhang, Luchin Chang, Songruoyao Wu, Xu Tan, Tao Qin, Tie-Yan Liu, and Kejun Zhang, “Relyme: Improving lyric-to-melody generation by incorporating lyric-melody re- lationships,” inProceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 1047–1056

2022
[27]

Fine-Tuning Language Models from Human Preferences

Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving, “Fine-tuning language models from human prefer- ences,”arXiv preprint arXiv:1909.08593, 2019

work page internal anchor Pith review arXiv 1909
[28]

Deep reinforcement learn- ing from human preferences,

Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei, “Deep reinforcement learn- ing from human preferences,”Advances in neural information processing systems, vol. 30, 2017

2017
[29]

Training language models to follow instructions with human feedback,

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agar- wal, Katarina Slama, Alex Ray, et al., “Training language models to follow instructions with human feedback,”Advances in neural information processing systems, vol. 35, pp. 27730– 27744, 2022

2022
[30]

Direct prefer- ence optimization: Your language model is secretly a reward model,

Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn, “Direct prefer- ence optimization: Your language model is secretly a reward model,”Advances in Neural Information Processing Systems, vol. 36, 2023

2023
[31]

KTO: Model Alignment as Prospect Theoretic Optimization

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Ju- rafsky, and Douwe Kiela, “Kto: Model alignment as prospect theoretic optimization,”arXiv preprint arXiv:2402.01306, 2024

work page internal anchor Pith review arXiv 2024
[32]

Qwen2.5 Technical Report

An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al., “Qwen2. 5 technical report,”arXiv preprint arXiv:2412.15115, 2024

work page internal anchor Pith review Pith/arXiv arXiv 2024
[33]

Techsinger: Technique controllable multilingual singing voice synthesis via flow matching,

Wenxiang Guo, Yu Zhang, Changhao Pan, Rongjie Huang, Li Tang, Ruiqi Li, Zhiqing Hong, Yongqi Wang, and Zhou Zhao, “Techsinger: Technique controllable multilingual singing voice synthesis via flow matching,” inProceedings of the AAAI Conference on Artificial Intelligence, 2025, vol. 39, pp. 23978–23986

2025