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HFEPX · Eval paper review

Predicting Music Valence and Arousal via Textual and Multimodal In-Context Learning in Large Language Models

Chi-En Dai, Chih-Pin Tan, Yi-Hsuan Yang

Published

Oct 22, 2026

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 22, 2026

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Demonstrations General

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

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Abstract

While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature. In this work, we investigate the potential of in-context learning (ICL) with large language models to advance continuous valence and arousal prediction. Rather than predicting emotion scores directly, our framework leverages contextual demonstrations from an auxiliary dataset to refine the initial predictions produced by a small, task-specific base model. We systematically evaluate a text-based ICL variant using lyrics and generated audio captions, alongside a multimodal variant that incorporates raw audio signals. Using the entire MER60 dataset strictly as a held-out test set, our experiments demonstrate substantial performance gains, particularly for valence. Text-based ICL improves the valence $R^2$ from 0.110 to 0.427 with 80 demonstration shots, while the multimodal variant achieves 0.346 with only 10 shots. These findings suggest that training-free ICL offers an effective inference-time strategy for current language models to predict continuous music emotion.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

partial

Demonstrations

Directly usable for protocol triage.

"Rather than predicting emotion scores directly, our framework leverages contextual demonstrations from an auxiliary dataset to refine the initial predictions produced by a small, task-specific base model."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature."

Quality Controls

missing

Not reported

No explicit QC controls found.

"While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Demonstrations
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature.
  • In this work, we investigate the potential of in-context learning (ICL) with large language models to advance continuous valence and arousal prediction.
  • Rather than predicting emotion scores directly, our framework leverages contextual demonstrations from an auxiliary dataset to refine the initial predictions produced by a small, task-specific base model.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Human demonstrations evaluation

Contribution summary

  • While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature.
  • In this work, we investigate the potential of in-context learning (ICL) with large language models to advance continuous valence and arousal prediction.
  • Rather than predicting emotion scores directly, our framework leverages contextual demonstrations from an auxiliary dataset to refine the initial predictions produced by a small, task-specific base model.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Demonstrations

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.