title: In-Context Prompt Editing For Conditional Audio Generation
summary: Distributional shift is a central challenge in the deployment of machine
  learning models as they can be ill-equipped for real-world data. This is
  particularly evident in text-to-audio generation where the encoded
  representations are easily undermined by unseen prompts, which leads to the
  degradation of generated audio -- the limited set of the text-audio pairs
  remains inadequate for conditional audio generation in the wild as user prompts
  are under-specified. In particular, we observe a consistent audio quality
  degradation in generated audio samples with user prompts, as opposed to
  training set prompts. To this end, we present a retrieval-based in-context
  prompt editing framework that leverages the training captions as demonstrative
  exemplars to revisit the user prompts. We show that the framework enhanced the
  audio quality across the set of collected user prompts, which were edited with
  reference to the training captions as exemplars.
paper_authors._id: 6545070ca28c87edcfa1ac01
paper_authors.name: Ernie Chang
paper_authors._id: 6545070ca28c87edcfa1ac02
paper_authors.name: Pin-Jie Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-03T15:01:17.151Z
paper_authors._id: 61c6133ed9ca62b30bb7726e
paper_authors.avatarUrl: /avatars/74471b38c62fef4233b34045868c71cf.svg
paper_authors.fullname: Pin-Jie Lin
paper_authors.type: user
paper_authors.user: linusdd44804
paper_authors._id: 6545070ca28c87edcfa1ac03
paper_authors.name: Yang Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-07T11:20:49.631Z
paper_authors._id: 654971d49b86bd6b2c6216ee
paper_authors.avatarUrl: /avatars/90fc2319c6d67694d4cd13401d9d2db3.svg
paper_authors.fullname: Yang Li
paper_authors.type: user
paper_authors.user: jerryyangli
paper_authors._id: 6545070ca28c87edcfa1ac04
paper_authors.name: Sidd Srinivasan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-03T15:06:33.331Z
paper_authors._id: 64e23052c3310dd6cc5e0f38
paper_authors.avatarUrl: /avatars/860a3cb6aedf722f1ff0e93b4c213e18.svg
paper_authors.fullname: Sidd Srinivasan
paper_authors.type: user
paper_authors.user: Sidds
paper_authors._id: 6545070ca28c87edcfa1ac05
paper_authors.name: Gael Le Lan
paper_authors._id: 6545070ca28c87edcfa1ac06
paper_authors.name: David Kant
paper_authors._id: 6545070ca28c87edcfa1ac07
paper_authors.name: Yangyang Shi
paper_authors._id: 6545070ca28c87edcfa1ac08
paper_authors.name: Forrest Iandola
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-03T15:07:16.917Z
paper_authors._id: 5f1753f4925b9863e28ad4de
paper_authors.avatarUrl: /avatars/e56d25a40f2c0e4e6696b9738877fce8.svg
paper_authors.fullname: Forrest Iandola
paper_authors.type: user
paper_authors.user: forresti
paper_authors._id: 6545070ca28c87edcfa1ac09
paper_authors.name: Vikas Chandra
paper_title: In-Context Prompt Editing For Conditional Audio Generation
paper_summary: Distributional shift is a central challenge in the deployment of machine
  learning models as they can be ill-equipped for real-world data. This is
  particularly evident in text-to-audio generation where the encoded
  representations are easily undermined by unseen prompts, which leads to the
  degradation of generated audio -- the limited set of the text-audio pairs
  remains inadequate for conditional audio generation in the wild as user prompts
  are under-specified. In particular, we observe a consistent audio quality
  degradation in generated audio samples with user prompts, as opposed to
  training set prompts. To this end, we present a retrieval-based in-context
  prompt editing framework that leverages the training captions as demonstrative
  exemplars to revisit the user prompts. We show that the framework enhanced the
  audio quality across the set of collected user prompts, which were edited with
  reference to the training captions as exemplars.