Scaling Audio-Text Retrieval with Multimodal Large Language Models

TL;DR

AuroLA introduces a scalable, MLLM-based framework for audio-text retrieval that outperforms existing models with less training data by leveraging diverse data, novel training losses, and deep cross-modal re-ranking.

Contribution

The paper presents a novel MLLM-based retrieval framework with a scalable data pipeline, hybrid loss, and re-ranking, significantly improving performance over state-of-the-art methods.

Findings

AuroLA outperforms state-of-the-art models like PE-AV.

It achieves comparable results with only 1% of PE-AV's training data.

Scaling dataset size and model capacity enhances retrieval performance.

Abstract

Audio-text retrieval is crucial for bridging acoustic signals and natural language. While contrastive dual-encoder architectures like CLAP have shown promise, they are fundamentally limited by the capacity of small-scale encoders. Specifically, the text encoders struggle to understand complex queries that require reasoning or world knowledge. In this paper, we propose AuroLA, a novel contrastive language-audio pre-training framework that re-purposes Multimodal Large Language Models (MLLMs) as a unified backbone for retrieval. Specifically, we make three contributions: (i) we construct a scalable data pipeline that curates diverse audio from multiple sources and generates multi-granular captions, ranging from long descriptions to structured tags, via automated annotation; (ii) we adapt an MLLM for retrieval by prompting it to summarize the audio/text input and using the hidden state of a special token as audio/text embeddings. For model training, we devise a novel Hybrid-NCE loss, which employs multi-granular supervision and hard-negative reweighting to robustly align audio with diverse textual supervision; and (iii) we design an MLLM-based bidirectional re-ranking module that refines retrieval candidates through deep cross-modal interaction. Extensive experiments demonstrate that AuroLA consistently outperforms state-of-the-art models, including the recent PE-AV, while utilizing only approximately 1% of PE-AV's training data. Lastly, we observe clear scaling trends regarding dataset size and model capacity, validating the effectiveness of MLLM as a unified backbone for audio-text retrieval. Code is available at https://github.com/Jazzcharles/AuroLA.