The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of
  Physical Concept Understanding
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.
67aeb180cb3be2cefd46ed07
Mo Yu
claimed_verified
2025-02-18T09:34:13.763Z
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Mo
user
BishopGorov
67aeb180cb3be2cefd46ed08
Lemao Liu
admin_assigned
2025-02-14T12:53:38.597Z
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/avatars/f9757030d82c69aef933309e0c83ccd0.svg
Lemao Liu
user
lemaoliu
67aeb180cb3be2cefd46ed09
Junjie Wu
admin_assigned
2025-02-14T12:53:53.052Z
64d36ca3036ae0b3756588e3
/avatars/0d7dfbd681b1157a38d0f0a86f19b702.svg
Junjie Wu
user
junjiewu
67aeb180cb3be2cefd46ed0a
Tsz Ting Chung
claimed_verified
2025-02-18T09:34:11.765Z
60ab6b2ee3de7c7440abb845
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Cindy
user
ttchungc
67aeb180cb3be2cefd46ed0b
Shunchi Zhang
admin_assigned
2025-02-14T12:54:03.865Z
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Shunchi Zhang
user
ShunchiZhang
67aeb180cb3be2cefd46ed0c
Jiangnan Li
67aeb180cb3be2cefd46ed0d
Dit-Yan Yeung
67aeb180cb3be2cefd46ed0e
Jie Zhou
The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of
  Physical Concept Understanding
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.
A study investigates whether large language models understand physical concepts through a grid-based task, showing that they significantly underperform compared to humans and highlighting the stochastic parrot phenomenon.
LLMs Stochastic Parrot PhysiCo grid-format inputs in-context learning fine-tuning