The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913689242697728 |
|---|---|
| author | Yu, Mo Liu, Lemao Wu, Junjie Chung, Tsz Ting Zhang, Shunchi Li, Jiangnan Yeung, Dit-Yan Zhou, Jie |
| author_facet | Yu, Mo Liu, Lemao Wu, Junjie Chung, Tsz Ting Zhang, Shunchi Li, Jiangnan Yeung, Dit-Yan Zhou, Jie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding Yu, Mo Liu, Lemao Wu, Junjie Chung, Tsz Ting Zhang, Shunchi Li, Jiangnan Yeung, Dit-Yan Zhou, Jie Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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. |
| title | The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2502.08946 |