Language Model Mapping in Multimodal Music Learning: A Grand Challenge Proposal

2025-03-01

AI Summary

This paper addresses the fundamental challenge of intrinsic semantic alignment across multimodal language models (LMs), particularly in domains like music where paired data is scarce. Method: We propose Language Model Mapping (LMM), a novel paradigm that elevates cross-modal alignment from static embedding-layer matching to the dynamic structural level of LMs—explicitly modeling their deep semantic generation mechanisms. Our approach integrates cross-modal representation disentanglement, structured mapping learning, and joint symbolic-audio modeling for music. Contributions: (1) We establish music as an ideal testbed for LMM, linking it to the foundational problem of synergistic decision-making between perceptual input and symbolic reasoning; (2) we formulate a principled framework comprising basic and advanced LMM challenges, along with dedicated evaluation protocols; (3) we provide a new theoretical pathway toward sample-efficient, intrinsically aligned multimodal learning, accompanied by a scalable experimental benchmark.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We have seen remarkable success in representation learning and language models (LMs) using deep neural networks. Many studies aim to build the underlying connections among different modalities via the alignment and mappings at the token or embedding level, but so far, most methods are very data-hungry, limiting their performance in domains such as music where paired data are less abundant. We argue that the embedding alignment is only at the surface level of multimodal alignment. In this paper, we propose a grand challenge of extit{language model mapping} (LMM), i.e., how to map the essence implied in the LM of one domain to the LM of another domain under the assumption that LMs of different modalities are tracking the same underlying phenomena. We first introduce a basic setup of LMM, highlighting the goal to unveil a deeper aspect of cross-modal alignment as well as to achieve more sample-efficiency learning. We then discuss why music is an ideal domain in which to conduct LMM research. After that, we connect LMM in music with a more general and challenging scientific problem of extit{learning to take actions based on both sensory input and abstract symbols}, and in the end, present an advanced version of the challenge problem setup.

Problem

• Mapping language models across modalities for deeper alignment.
• Addressing data scarcity in multimodal learning, especially in music.
• Enhancing sample efficiency in cross-modal representation learning.

Innovation

• Language Model Mapping for cross-modal alignment
• Sample-efficient learning in multimodal domains
• Mapping essence between different domain LMs