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Inferring Value Criteria from Ordinal Preferences: An Iterative In-Context Learning Framework for Music Generation

Futa Hidaka, Naomi Imasato, Kazuki Miyazawa, Takato Horii

arXiv:2608.30694v1cs.HC

TL;DR

The paper asks whether an LLM can learn an individual listener’s tacit musical criteria from rankings alone and transfer them beyond observed pieces. It uses iterative hypothesis-driven ABC generation with periodic natural-language criterion inference, finding that ranking-only adaptation succeeds only under limited conditions and transfers unevenly.

  • Problem

    The paper asks whether LLMs can adapt symbolic music generation from rankings alone and construct transferable descriptions of listeners’ tacit value criteria.

  • Method

    The framework has an LLM generate hypotheses and ABC candidates, receive rankings, and periodically infer verbalized value criteria from the accumulated history.

  • Results

    Across 480 cells with 16 simulated raters, the framework exceeded diverse generation only for targets rarely reached by simple sampling; unseen-music generation improved more broadly than preference prediction, which remained near chance.

  • Takeaways & Limitations

    Rankings alone can guide generation under limited conditions, but transferable criterion inference depends on the foundation model’s ability to recognize and verbalize musical features.

  • Takeaways & Limitations

    The study uses fixed target-specific functions, one LLM, one corpus, and limited Experiment 2 criteria, restricting conclusions about complex human preferences and broader generalization.

Abstract

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Adapting a generative music system to an individual's taste requires learning what that listener values. Listeners can rank pieces, but their underlying criteria may be tacit and difficult to articulate. We ask whether and under what conditions a large language model (LLM) can adapt symbolic music generation from rankings alone and construct transferable natural-language descriptions of value criteria. In our iterative in-context learning framework, the LLM formulates hypotheses, generates candidate pieces in ABC notation, receives a ranking, and periodically infers and verbalizes value criteria from history to guide later generation. We evaluate the framework against 16 simulated raters in 480 adaptation runs using mixed-effects modeling, an ablation, and transfer tests on unseen music. Overall, the framework did not outperform a feedback-free diverse-generation baseline, but did so for two value functions with targets difficult to reach through simple sampling. How atypical the target was relative to the LLM's feedback-free generation tendencies predicted adaptation difficulty. Moreover, higher value during adaptation did not imply identification of the criterion as a general rule. On unseen music, acquired descriptions and histories improved generation for more value functions than they improved preference prediction, which remained near chance. Some gains were associated with acoustic proximity to music in the context, but others were not. These findings show that rankings alone can guide generation under limited conditions, while transferable criterion inference remains constrained by the foundation model's ability to recognize, reason about, and verbalize musical attributes.

1 Introduction

The paper asks whether an LLM can learn an individual listener’s latent musical value criteria from rankings alone and reuse them for generation. It frames this as preference-based adaptation when criteria may be tacit and difficult to verbalize.

  • Motivation: Individual musical value depends on the evaluator, while existing preference-alignment methods need not match any particular person’s preferences.The paper motivates learning criteria specific to an individual listener rather than relying only on aggregate or automated preferences.
  • Motivation: Text-feedback methods assume raters can explain their choices, but preference-only methods avoid that assumption while receiving less information and potentially noisier signals.The paper notes that some preference criteria may be tacit, limiting the reliability of verbal explanations.
  • Conceptual framework: The framework defines value as a rater’s assigned desirability and value criteria as latent rules linking musical attributes to desirability.These criteria cannot be observed directly, so the system learns them through trial and error from rankings.
  • Proposed direction: The LLM generates candidate music, receives rankings without explanations, and periodically infers a verbalized working hypothesis from the accumulated history.The description is then reused to guide subsequent generation.
  • Research questions: The study asks whether ranking-only adaptation works, whether acquired descriptions and histories transfer to unseen music, and which factors affect adaptation and transfer.The contribution combines an LLM framework with controlled evaluations and analyses using simulated raters.

2 Related Work

Prior work adapts music systems from evaluations, infers latent preferences, and uses LLMs to optimize from interaction histories. This paper focuses on constructing transferable natural-language criteria from rankings alone.

  • Preference-based music adaptation: Online music adaptation methods improve generation through continued evaluations, whereas offline preference alignment learns from large preference sets or automated proxy evaluators.The distinction is between ongoing individual interaction and training from accumulated preferences.
  • Latent criteria inference: Preference-learning approaches commonly infer utility as weights over predefined features, while some work uses LLMs to identify domain-specific features.Learning the features themselves is needed when existing feature spaces cannot explain feedback.
  • LLM-based optimization: LLM-based optimization has used histories and feedback, with prior findings that binary-only feedback can degrade performance in some settings.Accumulated context has also outperformed direct prompting with stated criteria in a vector-graphics task.
  • Natural-language criteria: Natural-language preference criteria have been extracted or refined through pairwise data, conversation, and explanation elicitation, but several approaches rely on verbal reports.Methods avoiding reports have generally targeted objectively verifiable rules rather than tacit musical preferences.
  • Gap addressed: This work examines whether a working hypothesis formed from rankings alone transfers to prediction and generation on unseen music.The transfer question extends beyond adapting to pieces observed during interaction.

3 Proposed Method

The proposed system iteratively generates three ABC-notation candidates from LLM hypotheses, obtains a ranking against the current reference, and periodically verbalizes inferred value criteria for later iterations.

  • Iterative context: Each iteration supplies the LLM with the reference piece, ranking history, and latest value-criteria description before generating hypotheses and music.The history retains hypotheses, ABC scores, and rankings from prior iterations.
  • Hypothesis generation: The LLM forms three hypotheses about musical modifications, including falsification-style changes that may temporarily reduce value while testing criteria.Each hypothesis is retained with its generated score and resulting ranking for later inference.
  • Algorithm: The system’s procedure initializes a reference and empty history, repeats hypothesis generation, music generation, ranking, and history updates, and periodically updates the description.This sequence is formalized in Algorithm 1.
  • Music generation: One ABC score is generated from each hypothesis, yielding three candidate pieces per iteration.The candidates are evaluated alongside the reference piece.
  • Evaluation: The rater ranks the three new pieces and the reference, with ties resolved so that a tied new piece can replace the reference.Ties among new pieces are resolved in generation order.
  • Criteria inference: Every five iterations, the LLM infers and verbalizes criteria from the ranking history and past descriptions, then adds the resulting description to shared context.The inference prompt directs attention to ABC scores as well as natural-language hypotheses and descriptions.

4 Experimental Setup

The experiments use simulated value functions to evaluate iterative adaptation and transfer, with a shared corpus of 60 filtered pieces. The functions score target attributes from 0 to 100, including single features and jointly required combinations.

  • Evaluation design: Experiment 1 evaluates iterative adaptation, while Experiment 2 evaluates transfer of acquired criteria to unseen music using 16 value functions and 60 pieces.The value functions implement the simulated raters.
  • Value functions: Each value function scores music from 0 to 100, assigning 100 to its target state and lower values as deviation increases.The study includes 13 single functions and three pairwise combinations spanning symbolic and acoustic measures.
  • Value functions: The functions include pitch range, pitch, tempo, duration, contour, pitch class, and an audio-based sadness measure from Music2Emo.Different functions use exponential distance decay or proportions satisfying the target.
  • Value functions: Combination functions use the geometric mean √v_a · v_b, requiring strong simultaneous achievement of both constituent targets.This design tests preferences involving multiple musical features.
  • Music corpus: The dataset contains 60 OpenScore Lieder Corpus pieces, split into 30 initial pieces and 30 unseen test pieces after ABC conversion and filtering.Filtering required successful conversion and rendering, scores of at most 3,000 bytes, durations of 10–100 seconds, and at least 50 MIDI note-on events.

5 Experiment 1: Iterative Adaptation from Ranking Preferences

Experiment 1 evaluates iterative ranking-based adaptation against diverse generation across 480 cells, finding no overall advantage but significant gains for two value functions. Adaptation difficulty was related to target atypicality and initial value gap, while verbalized criteria added little during adaptation.

  • 5.2.1 Trajectory of iterative adaptation and comparison with diverse generation: 480 cells covered 16 value functions and 30 initial pieces, with 20 iterations generating three candidates from three hypotheses per iteration.The diverse-generation baseline used the same number of generated pieces without value-function history.
  • 5.1 Purpose and setup: The adaptation ratio measures the percentage of the initial-to-maximum value gap closed by each iteration, with comparisons made at iteration 20.All 480 cells were analyzed because none had an initial value of 100.
  • 5.2.1 Trajectory of iterative adaptation and comparison with diverse generation: 51.75 was the mean adaptation ratio at iteration 20, following 25.25 at iteration 1 and 46.43 at iteration 10, with large early gains and continued later improvement.The final standard deviation was 29.04, and trajectories varied substantially across value functions.
  • 5.2.1 Trajectory of iterative adaptation and comparison with diverse generation: β = −10.31 against the diverse-generation mean of 62.06, so the proposed system did not surpass the baseline overall; it was higher for C2–B2 range and Ascending contour × 150 BPM tempo only.The two significant mean differences were 13.76 and 15.45, respectively, with no advantage for the remaining 14 functions.
  • 5.2.2 Additional analyses of adaptation difficulty: β = −11.51 for target atypicality and β = 6.43 for initial value gap, indicating lower adaptation for atypical targets and higher normalized ratios when more improvement remained.Variation across value functions unexplained by atypicality and residual cell-level variation were both large.
  • 5.2.3 Ablation of the value-criteria description: +0.13 was the average ablation difference from removing the value-criteria description, with only C2–B2 range significant after Holm correction and no evidence of hindrance.Because ranking-history entries were held constant, the description’s information may have been redundant with the history during adaptation.

6 Experiment 2: Transfer of Acquired Value Criteria

Experiment 2 tested whether descriptions and histories from adaptation transfer to unseen-music preference prediction and generation. Transfer was stronger for generation than prediction: accuracy stayed near chance, while condition (iv) generated higher-value music for several functions.

  • Experiment 2 tested transfer of Experiment 1 descriptions and histories to preference prediction and generation on unseen music.
  • Prediction accuracy: Mean prediction accuracy stayed near chance, ranging from 0.46 to 0.62 across conditions and value functions.
  • Prediction accuracy: Condition (iv) significantly exceeded other conditions for C2–B2 range, selected comparisons for Ascending contour × 150 BPM tempo, and condition (ii) for 60 s duration.
  • Transfer diagnostics: Successful adaptation and internally consistent choices did not ensure accurate transfer, and condition (iv)’s disagreement across presentation orders was 25.24% versus 40.92% in condition (i).
  • Generated value: Condition (iv) produced the highest or tied-highest mean value for all five functions and significantly exceeded condition (v) only for C2–B2 range and Ascending contour × 150 BPM tempo.
  • Generated value: For 60 s duration, condition (iv) reached 24.39 against 6.62 for condition (i), while condition (v) exceeded condition (iv) for M2E-Sad.
  • Transfer diagnostics: Some generated-value gains were associated with acoustic proximity to reference pieces, but gains also occurred without such proximity.

7 Discussion

Ranking-only adaptation surpassed diverse generation only for targets rarely reached by simple sampling, and higher generated value did not establish a generalizable criterion. Transfer was task-dependent, with generation improving for more functions than preference prediction, while effectiveness depended on the model, representation, and target.

  • Synthesis of the main findings: Adaptation from rankings alone surpassed diverse generation only for targets rarely reached by simple sampling under the diversity prompt.
  • Synthesis of the main findings: Higher generated value did not imply identifying a generalizable criterion, because the LLM could exploit local cues or specific note sequences.
  • Synthesis of the main findings: Transfer was task-dependent: acquired context improved generated value for more functions, while preference prediction remained near chance.
  • Factors affecting adaptation and transfer: Adaptation difficulty was associated with target distance from feedback-free generation tendencies and the available improvement margin.
  • Factors affecting adaptation and transfer: Acoustic proximity to referenced pieces accompanied some value gains but not others, suggesting difficulty varies with the rater, foundation model, and music representation.
  • Strengths and trade-offs: The system guides generation toward rarely sampled targets under a matched budget of 61 unique evaluated pieces and retains reusable histories and criteria descriptions.
  • Strengths and trade-offs: Hypothesis- and language-mediated generation may narrow exploration toward local, easily verbalized features, motivating combination with example-driven generation.
  • Limitations: The study uses fixed target-specific functions, one LLM, one corpus, five Experiment 2 functions, and ABC notation, limiting generalization to complex preferences, other models, styles, criteria, and acoustic attributes.

8 Conclusion

The paper proposes ranking-only iterative in-context learning for constructing natural-language value criteria in music generation. Across simulated-rater experiments, benefits were limited to difficult-to-sample targets and favored generation transfer over preference prediction.

  • Across 480 cells with 16 simulated raters, the framework exceeded diverse generation only for targets rarely reached by simple sampling.
  • Acquired context improved generation for unseen music, while preference prediction remained near chance.
  • The effectiveness and limits of ranking-only adaptation depend on the foundation model’s ability to recognize and verbalize musical features.

9 Reproducibility

The experiments used deterministic simulated raters, a fixed model and corpus, and documented implementation settings for reproducibility.

  • The evaluation used fully specified value functions as deterministic simulated raters, gpt-oss-120b, and 60 pieces from the OpenScore Lieder Corpus.
  • Appendix E reports decoding, seed, model-revision, and software settings, while the corpus source commit and audio-rendering details are recorded.

Artificial Intelligence Use

Generative AI tools supported translation, language editing, and an initial visual draft of Figure 2, which the authors later reconstructed and manually revised.

  • Generative AI tools were used for translation and language editing.
  • An initial visual draft of Figure 2 was generated with AI and subsequently reconstructed and manually revised.
  • The authors reviewed and approved the scientific claims, analyses, citations, final text, and figures.

Supplementary Material

The supplementary material reproduces the prompts and operational details governing the proposed LLM music-generation procedure, including hypothesis formation, ABC composition, and value-criteria inference.

  • Prompt materials: The supplementary appendix reproduces prompts for the proposed system and the diverse-generation baseline verbatim.It documents shared system instructions, hypothesis generation, music generation, value-criteria inference, and baseline generation prompts.
  • Iterative procedure: Every five iterations, the LLM constructs a tentative value-criteria description from the accumulated ranking history.The description remains a working hypothesis derived from trial and error and is empty until first generated.
  • Prompt variables: Runtime placeholders insert the initial ABC score, ranking history, iteration number, hypotheses, and latest value-criteria description into prompts.The diverse-generation baseline instead expands sequences of recent ABC scores and tracks the number and index of generated pieces.
  • Iterative procedure: The proposed system iteratively hypothesizes about evaluator preferences, composes new ABC pieces, and uses ranking history to guide later generations.The system’s composer prompt explicitly repeats hypothesis formation and ABC composition based on prior evaluations.
  • ABC constraints: ABC output must follow strict syntax rules covering headers, voices, chords, repeats, bar markers, octaves, MIDI settings, decorations, rests, note lengths, ties, and tuplets.These constraints are intended to produce valid scores for the ABC-to-MIDI workflow.

C.1 Prediction Task and Diagnostics

Experiment 2 found limited and uneven transfer to unseen-music preference prediction: accuracy remained near chance, while some conditions outperformed others for selected value functions. Generation gains were partly associated with acoustic proximity, but within-loop adaptation did not reliably predict transfer.

  • Transfer diagnostics: Within-loop adaptation did not predict preference prediction on unseen music: none of the corrected Pearson or Spearman correlations was significant.Correlation directions were inconsistent across value functions.
  • Prediction diagnostics: Larger value differences showed function-dependent relations with accuracy, including weak negative relations for 60 s duration and M2E-Sad.The clearest positive relations occurred for C2–B2 range and Ascending contour × 150 BPM tempo.
  • Acoustic proximity: In condition (iv), closer generations had higher value within cells, and greater history-induced proximity gains tracked larger value gains for three of five functions.These associations were correlational; Ascending contour × 150 BPM tempo showed a value gain unrelated to proximity.
  • Transfer diagnostics: Evidence that strong Experiment 1 adaptation transferred to generation was limited, with only condition (iii) for C2–B2 range significant after Holm correction.That association was Pearson r = 0.617.

Appendix D: Illustrative Case Analyses from Experiment 1

The appendix presents four illustrative adaptation cases showing distinct ways hypotheses and musical changes produced higher values. These cases are exploratory rather than statistically representative, and one trajectory is visualized for M2E-Sad.

  • Interpretation: The cases are qualitatively different and not statistically representative, so their trajectories illustrate possible adaptation processes rather than general mechanisms.The appendix explicitly cautions that the cases do not establish a general mechanism.
  • Case overview: Four cases illustrate stepwise exploration, accidental-success retention, implicit inheritance without verbalization, and evaluation-model-specific cue acquisition.The cases cover 150 BPM tempo, Ascending contour with music7 and music10, and M2E-Sad.
  • Ascending contour: In both Ascending contour cases, simplifying pitch content coincided with the emergence of ascending sequences.The appendix notes this shared trajectory may partly explain the ascending–descending asymmetry observed in Experiment 1.
  • Visualizations: Figure D1 shows piano-roll trajectories for the initial piece and iterations 1, 2, and 16 in the M2E-Sad case.The figure provides a visual trajectory for one of the four illustrative cases.
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