AC/DC: LLM-based Audio Comprehension via Dialogue Continuation

• URL: http://arxiv.org/abs/2506.10312v1
• Date: Thu, 12 Jun 2025 02:48:47 GMT
• Title: AC/DC: LLM-based Audio Comprehension via Dialogue Continuation
• Authors: Yusuke Fujita, Tomoya Mizumoto, Atsushi Kojima, Lianbo Liu, Yui Sudo,
• Abstract summary: We propose an instruction-following audio comprehension model that leverages the dialogue continuation ability of large language models (LLMs)<n>Our model enables zero-shot instruction-following capability without multitask instruction tuning, even trained solely on audio captioning datasets.
• Score: 8.171886468845049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an instruction-following audio comprehension model that leverages the dialogue continuation ability of large language models (LLMs). Instead of directly generating target captions in training data, the proposed method trains a model to produce responses as if the input caption triggered a dialogue. This dialogue continuation training mitigates the caption variation problem. Learning to continue a dialogue effectively captures the caption's meaning beyond its surface-level words. As a result, our model enables zero-shot instruction-following capability without multitask instruction tuning, even trained solely on audio captioning datasets. Experiments on AudioCaps, WavCaps, and Clotho datasets with AudioBench audio-scene question-answering tests demonstrate our model's ability to follow various unseen instructions.

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