title: The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of
    Physical Concept Understanding
summary: In a systematic way, we investigate a widely asked question: Do LLMs really
  understand what they say?, which relates to the more familiar term Stochastic
  Parrot. To this end, we propose a summative assessment over a carefully
  designed physical concept understanding task, PhysiCo. Our task alleviates the
  memorization issue via the usage of grid-format inputs that abstractly describe
  physical phenomena. The grids represents varying levels of understanding, from
  the core phenomenon, application examples to analogies to other abstract
  patterns in the grid world. A comprehensive study on our task demonstrates: (1)
  state-of-the-art LLMs, including GPT-4o, o1 and Gemini 2.0 flash thinking, lag
  behind humans by ~40%; (2) the stochastic parrot phenomenon is present in LLMs,
  as they fail on our grid task but can describe and recognize the same concepts
  well in natural language; (3) our task challenges the LLMs due to intrinsic
  difficulties rather than the unfamiliar grid format, as in-context learning and
  fine-tuning on same formatted data added little to their performance.
paper_authors._id: 67aeb180cb3be2cefd46ed07
paper_authors.name: Mo Yu
paper_authors.status: claimed_verified
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paper_authors.fullname: Mo
paper_authors.type: user
paper_authors.user: BishopGorov
paper_authors._id: 67aeb180cb3be2cefd46ed08
paper_authors.name: Lemao Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: Lemao Liu
paper_authors.type: user
paper_authors.user: lemaoliu
paper_authors._id: 67aeb180cb3be2cefd46ed09
paper_authors.name: Junjie Wu
paper_authors.status: admin_assigned
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paper_authors.fullname: Junjie Wu
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paper_authors.user: junjiewu
paper_authors._id: 67aeb180cb3be2cefd46ed0a
paper_authors.name: Tsz Ting Chung
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:34:11.765Z
paper_authors._id: 60ab6b2ee3de7c7440abb845
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paper_authors.fullname: Cindy
paper_authors.type: user
paper_authors.user: ttchungc
paper_authors._id: 67aeb180cb3be2cefd46ed0b
paper_authors.name: Shunchi Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-14T12:54:03.865Z
paper_authors._id: 63fe24448b3c5087ff866b39
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63fe24448b3c5087ff866b39/CaTM4yAfj9tJj53ciQWJk.jpeg
paper_authors.fullname: Shunchi Zhang
paper_authors.type: user
paper_authors.user: ShunchiZhang
paper_authors._id: 67aeb180cb3be2cefd46ed0c
paper_authors.name: Jiangnan Li
paper_authors._id: 67aeb180cb3be2cefd46ed0d
paper_authors.name: Dit-Yan Yeung
paper_authors._id: 67aeb180cb3be2cefd46ed0e
paper_authors.name: Jie Zhou
paper_title: The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of
    Physical Concept Understanding
paper_summary: In a systematic way, we investigate a widely asked question: Do LLMs really
  understand what they say?, which relates to the more familiar term Stochastic
  Parrot. To this end, we propose a summative assessment over a carefully
  designed physical concept understanding task, PhysiCo. Our task alleviates the
  memorization issue via the usage of grid-format inputs that abstractly describe
  physical phenomena. The grids represents varying levels of understanding, from
  the core phenomenon, application examples to analogies to other abstract
  patterns in the grid world. A comprehensive study on our task demonstrates: (1)
  state-of-the-art LLMs, including GPT-4o, o1 and Gemini 2.0 flash thinking, lag
  behind humans by ~40%; (2) the stochastic parrot phenomenon is present in LLMs,
  as they fail on our grid task but can describe and recognize the same concepts
  well in natural language; (3) our task challenges the LLMs due to intrinsic
  difficulties rather than the unfamiliar grid format, as in-context learning and
  fine-tuning on same formatted data added little to their performance.
paper_ai_keywords: LLMs Stochastic Parrot PhysiCo grid-format inputs grid world core phenomenon application examples Analogies state-of-the-art LLMs GPT-4o, o1 Gemini 2 flash forward in-context learning fine-tuning