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Distributional shift is a central challenge in the deployment of machinelearning models as they can be ill-equipped for real-world data. This isparticularly evident in text-to-audio generation where the encodedrepresentations are easily undermined by unseen prompts, which leads to thedegradation of generated audio -- the limited set of the text-audio pairsremains inadequate for conditional audio generation in the wild as user promptsare under-specified. In particular, we observe a consistent audio qualitydegradation in generated audio samples with user prompts, as opposed totraining set prompts. To this end, we present a retrieval-based in-contextprompt editing framework that leverages the training captions as demonstrativeexemplars to revisit the user prompts. We show that the framework enhanced theaudio quality across the set of collected user prompts, which were edited withreference to the training captions as exemplars.

In-Context Prompt Editing For Conditional Audio Generation