Content area
Recent advances in audio understanding tasks leverage the reasoning capabilities of LLMs. However, adapting LLMs to learn audio concepts requires massive training data and substantial computational resources. To address these challenges, Retrieval-Augmented Generation (RAG) retrieves audio-text pairs from a knowledge base (KB) and augments them with query audio to generate accurate textual responses. In RAG, the relevance of the retrieved information plays a crucial role in effectively processing the input. In this paper, we analyze how different retrieval methods and knowledge bases impact the relevance of audio-text pairs and the performance of audio captioning with RAG. We propose generative pair-to-pair retrieval, which uses the generated caption as a text query to accurately find relevant audio-text pairs to the query audio, thereby improving the relevance and accuracy of retrieved information. Additionally, we refine the large-scale knowledge base to retain only audio-text pairs that align with the contextualized intents. Our approach achieves state-of-the-art results on benchmarks including AudioCaps, Clotho, and Auto-ACD, with detailed ablation studies validating the effectiveness of our retrieval and KB construction methods.