title: The Generative AI Paradox: "What It Can Create, It May Not Understand"
summary: The recent wave of generative AI has sparked unprecedented global attention,
  with both excitement and concern over potentially superhuman levels of
  artificial intelligence: models now take only seconds to produce outputs that
  would challenge or exceed the capabilities even of expert humans. At the same
  time, models still show basic errors in understanding that would not be
  expected even in non-expert humans. This presents us with an apparent paradox:
  how do we reconcile seemingly superhuman capabilities with the persistence of
  errors that few humans would make? In this work, we posit that this tension
  reflects a divergence in the configuration of intelligence in today's
  generative models relative to intelligence in humans. Specifically, we propose
  and test the Generative AI Paradox hypothesis: generative models, having been
  trained directly to reproduce expert-like outputs, acquire generative
  capabilities that are not contingent upon -- and can therefore exceed -- their
  ability to understand those same types of outputs. This contrasts with humans,
  for whom basic understanding almost always precedes the ability to generate
  expert-level outputs. We test this hypothesis through controlled experiments
  analyzing generation vs. understanding in generative models, across both
  language and image modalities. Our results show that although models can
  outperform humans in generation, they consistently fall short of human
  capabilities in measures of understanding, as well as weaker correlation
  between generation and understanding performance, and more brittleness to
  adversarial inputs. Our findings support the hypothesis that models' generative
  capability may not be contingent upon understanding capability, and call for
  caution in interpreting artificial intelligence by analogy to human
  intelligence.
paper_authors._id: 65430bc1e48d91b86947f570
paper_authors.name: Peter West
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paper_authors.name: Ximing Lu
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paper_authors.name: Nouha Dziri
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paper_authors.fullname: Nouha Dziri
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paper_authors.name: Faeze Brahman
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paper_authors.fullname: Faeze Brahman
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paper_authors.user: faezeb
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paper_authors.name: Linjie Li
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/bfb7e0d730b7d03302799d5d2828d97d.svg
paper_authors.fullname: Linjie Li
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paper_authors.user: linjieli222
paper_authors._id: 65430bc1e48d91b86947f575
paper_authors.name: Jena D. Hwang
paper_authors._id: 65430bc1e48d91b86947f576
paper_authors.name: Liwei Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-02T13:35:49.725Z
paper_authors._id: 5efe5c89ff69163f6f59e628
paper_authors.avatarUrl: /avatars/c7ea475c50fcce528c9b748f279058eb.svg
paper_authors.fullname: Liwei Jiang
paper_authors.type: user
paper_authors.user: liweijiang
paper_authors._id: 65430bc1e48d91b86947f577
paper_authors.name: Jillian Fisher
paper_authors._id: 65430bc1e48d91b86947f578
paper_authors.name: Abhilasha Ravichander
paper_authors.status: admin_assigned
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paper_authors._id: 6349886c429608888c42319a
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paper_authors.fullname: Abhilasha Ravichander
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paper_authors.user: lasha-nlp
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paper_authors.name: Khyathi Chandu
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paper_authors.name: Benjamin Newman
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paper_authors.name: Pang Wei Koh
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paper_authors.fullname: Pang Wei Koh
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paper_authors.name: Allyson Ettinger
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paper_authors.fullname: Allyson Ettinger
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paper_authors.name: Yejin Choi
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paper_authors.fullname: Yejin Choi
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paper_title: The Generative AI Paradox: "What It Can Create, It May Not Understand"
paper_summary: The recent wave of generative AI has sparked unprecedented global attention,
  with both excitement and concern over potentially superhuman levels of
  artificial intelligence: models now take only seconds to produce outputs that
  would challenge or exceed the capabilities even of expert humans. At the same
  time, models still show basic errors in understanding that would not be
  expected even in non-expert humans. This presents us with an apparent paradox:
  how do we reconcile seemingly superhuman capabilities with the persistence of
  errors that few humans would make? In this work, we posit that this tension
  reflects a divergence in the configuration of intelligence in today's
  generative models relative to intelligence in humans. Specifically, we propose
  and test the Generative AI Paradox hypothesis: generative models, having been
  trained directly to reproduce expert-like outputs, acquire generative
  capabilities that are not contingent upon -- and can therefore exceed -- their
  ability to understand those same types of outputs. This contrasts with humans,
  for whom basic understanding almost always precedes the ability to generate
  expert-level outputs. We test this hypothesis through controlled experiments
  analyzing generation vs. understanding in generative models, across both
  language and image modalities. Our results show that although models can
  outperform humans in generation, they consistently fall short of human
  capabilities in measures of understanding, as well as weaker correlation
  between generation and understanding performance, and more brittleness to
  adversarial inputs. Our findings support the hypothesis that models' generative
  capability may not be contingent upon understanding capability, and call for
  caution in interpreting artificial intelligence by analogy to human
  intelligence.
paper_ai_keywords: generative AI expert-like outputs basic understanding generative capabilities expert-level outputs controlled experiments adversarial inputs understanding capability