# Investigating Modality Contribution in Audio LLMs for Music

arXiv: https://arxiv.org/abs/2509.20641 · HTML: https://mlsanity.com/paper/2509.20641/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 28 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 23 of 23)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [SALMONN: Towards Generic Hearing Abilities for Large Language Models](https://mlsanity.com/paper/2310.13289/) (2023)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2410.19168/) (2024)
- [Pengi: An Audio Language Model for Audio Tasks](https://mlsanity.com/paper/2305.11834/) (2023)
- [Listen, Think, and Understand](https://mlsanity.com/paper/2305.10790/) (2023)
- [Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning](https://mlsanity.com/paper/2308.11276/) (2023)
- [On Measuring Faithfulness or Self-consistency of Natural Language Explanations](https://mlsanity.com/paper/2311.07466/) (2023)
- [Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!](https://mlsanity.com/paper/2010.06572/) (2020)
- [MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response](https://mlsanity.com/paper/2309.08730/) (2023)
- [MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models](https://mlsanity.com/paper/2408.01337/) (2024)
- [MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Tasks](https://mlsanity.com/paper/2212.08158/) (2022)
- [CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models](https://mlsanity.com/paper/2310.08753/) (2023)
- [Perceptual Score: What Data Modalities Does Your Model Perceive?](https://mlsanity.com/paper/2110.14375/) (2021)
- [DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations](https://mlsanity.com/paper/2203.02013/) (2022)
- [Do Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?](https://mlsanity.com/paper/2404.18624/) (2024)
- [audioLIME: Listenable Explanations Using Source Separation](https://mlsanity.com/paper/2008.00582/) (2020)
- [Reliable Local Explanations for Machine Listening](https://mlsanity.com/paper/2005.07788/) (2020)
- [Are you really listening? Boosting Perceptual Awareness in Music-QA Benchmarks](https://mlsanity.com/paper/2504.00369/) (2025)
- [MusicLIME: Explainable Multimodal Music Understanding](https://mlsanity.com/paper/2409.10496/) (2024)
- [Benchmarking Time-localized Explanations for Audio Classification Models](https://mlsanity.com/paper/2506.04391/) (2025)

## Built on by (most cited first, 2 of 2)

- [Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition](https://mlsanity.com/paper/2603.12046/) (2026)
- [Can LLMs understand LilyPond? A benchmark for symbolic music generation and understanding](https://mlsanity.com/paper/2606.08722/) (2026)

## Abstract (arXiv)

Audio Large Language Models (Audio LLMs) enable human-like conversation about music, yet it is unclear if they are truly listening to the audio or just using textual reasoning, as recent benchmarks suggest. This paper investigates this issue by quantifying the contribution of each modality to a model's output. We adapt the MM-SHAP framework, a performance-agnostic score based on Shapley values that quantifies the relative contribution of each modality to a model's prediction. We evaluate two models on the MuChoMusic benchmark and find that the model with higher accuracy relies more on text to answer questions, but further inspection shows that even if the overall audio contribution is low, models can successfully localize key sound events, suggesting that audio is not entirely ignored. Our study is the first application of MM-SHAP to Audio LLMs and we hope it will serve as a foundational step for future research in explainable AI and audio.