MUST-RAG: MUSical Text Question Answering with Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2507.23334v1
• Date: Thu, 31 Jul 2025 08:31:05 GMT
• Title: MUST-RAG: MUSical Text Question Answering with Retrieval Augmented Generation
• Authors: Daeyong Kwon, SeungHeon Doh, Juhan Nam,
• Abstract summary: MusT-RAG is a comprehensive framework based on Retrieval Augmented Generation (RAG)<n>MusWikiDB is a music-specialized vector database for the retrieval stage.<n>Our experiment demonstrates that MusT-RAG significantly outperforms traditional fine-tuning approaches in enhancing LLMs' music domain adaptation capabilities.
• Score: 6.903890310699392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in Large language models (LLMs) have demonstrated remarkable capabilities across diverse domains. While they exhibit strong zero-shot performance on various tasks, LLMs' effectiveness in music-related applications remains limited due to the relatively small proportion of music-specific knowledge in their training data. To address this limitation, we propose MusT-RAG, a comprehensive framework based on Retrieval Augmented Generation (RAG) to adapt general-purpose LLMs for text-only music question answering (MQA) tasks. RAG is a technique that provides external knowledge to LLMs by retrieving relevant context information when generating answers to questions. To optimize RAG for the music domain, we (1) propose MusWikiDB, a music-specialized vector database for the retrieval stage, and (2) utilizes context information during both inference and fine-tuning processes to effectively transform general-purpose LLMs into music-specific models. Our experiment demonstrates that MusT-RAG significantly outperforms traditional fine-tuning approaches in enhancing LLMs' music domain adaptation capabilities, showing consistent improvements across both in-domain and out-of-domain MQA benchmarks. Additionally, our MusWikiDB proves substantially more effective than general Wikipedia corpora, delivering superior performance and computational efficiency.

Related papers

• MuCPT: Music-related Natural Language Model Continued Pretraining [2.2288022262475873]
  We build a large, music-related natural language corpus (40B tokens) that combines open source and in-house data.<n>We also introduce reference-model (RM)-based token-level soft scoring for quality control.<n>Overall, this work advances both the right corpus and the right objective, offering a scalable data-training framework.
  arXiv  Detail & Related papers  (2025-11-18T08:33:34Z)
• GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models [59.72897499248909]
  We propose a novel graph retriever trained end-to-end with Large Language Models (LLMs)<n>Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together.<n>Our approach consistently achieves state-of-the-art performance, validating the strength of joint graph-LLM optimization for complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-09-20T02:38:00Z)
• General-Reasoner: Advancing LLM Reasoning Across All Domains [64.70599911897595]
  Reinforcement learning (RL) has recently demonstrated strong potential in enhancing the reasoning capabilities of large language models (LLMs)<n>We propose General-Reasoner, a novel training paradigm designed to enhance LLM reasoning capabilities across diverse domains.<n>We train a series of models and evaluate them on a wide range of datasets covering wide domains like physics, chemistry, finance, electronics etc.
  arXiv  Detail & Related papers  (2025-05-20T17:41:33Z)
• Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation [52.8352968531863]
  Large language models (LLMs) have made significant progress in general-purpose natural language processing tasks.<n>This paper presents a novel framework that combines knowledge graph (KG) and retrieval-augmented generation (RAG) techniques to enhance LLM performance in the telecom domain.
  arXiv  Detail & Related papers  (2025-03-31T15:58:08Z)
• mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA [78.45521005703958]
  multimodal Retrieval-Augmented Generation (mRAG) is naturally introduced to provide MLLMs with comprehensive and up-to-date knowledge. We propose a novel framework called textbfRetrieval-textbfReftextbfAugmented textbfGeneration (mR$2$AG) which achieves adaptive retrieval and useful information localization. mR$2$AG significantly outperforms state-of-the-art MLLMs on INFOSEEK and Encyclopedic-VQA
  arXiv  Detail & Related papers  (2024-11-22T16:15:50Z)
• Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation [43.630437906898635]
  We propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information.
  arXiv  Detail & Related papers  (2024-11-11T14:25:37Z)
• SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains [45.349645606978434]
  Retrieval-augmented generation (RAG) enhances the question-answering abilities of large language models (LLMs)<n>We propose SimRAG, a self-training approach that equips the LLM with joint capabilities of question answering and question generation for domain adaptation.<n> Experiments on 11 datasets, spanning two backbone sizes and three domains, demonstrate that SimRAG outperforms baselines by 1.2%--8.6%.
  arXiv  Detail & Related papers  (2024-10-23T15:24:16Z)
• Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization [7.522493227357079]
  Large Language Models (LLMs) are pre-trained on large-scale corpora. LLMs suffer from hallucinations, knowledge cut-offs, and lack of knowledge attributions. We introduce SMART-SLIC, a highly domain-specific LLM framework.
  arXiv  Detail & Related papers  (2024-10-03T17:40:55Z)
• MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation [60.04380907045708]
  Retrieval-Augmented Generation (RAG) is considered a promising strategy to address this problem.<n>We propose MemoRAG, a novel RAG framework empowered by global memory-augmented retrieval.<n>MemoRAG achieves superior performances across a variety of long-context evaluation tasks.
  arXiv  Detail & Related papers  (2024-09-09T13:20:31Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs)<n>We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation [128.01050030936028]
  We propose an information refinement training method named InFO-RAG. InFO-RAG is low-cost and general across various tasks. It improves the performance of LLaMA2 by an average of 9.39% relative points.
  arXiv  Detail & Related papers  (2024-02-28T08:24:38Z)
• Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations [50.81844184210381]
  We propose a general paradigm that augments large language models with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE. This paradigm relies on a domain knowledge extractor, working in three steps: 1) preparing effective knowledge for the task; 2) selecting the knowledge for each specific sample; and 3) expressing the knowledge in an LLM-understandable way.
  arXiv  Detail & Related papers  (2023-11-16T07:09:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.