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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.03503 |
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| _version_ | 1866915603281870848 |
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| author | Naim, Omar Bolte, Jerome Asher, Nicholas |
| author_facet | Naim, Omar Bolte, Jerome Asher, Nicholas |
| contents | Our paper challenges claims from prior research that transformer-based models, when learning in context, implicitly implement standard learning algorithms. We present empirical evidence inconsistent with this view and provide a mathematical analysis demonstrating that transformers cannot achieve general predictive accuracy due to inherent architectural limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03503 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyzing limits for in-context learning Naim, Omar Bolte, Jerome Asher, Nicholas Machine Learning Artificial Intelligence Our paper challenges claims from prior research that transformer-based models, when learning in context, implicitly implement standard learning algorithms. We present empirical evidence inconsistent with this view and provide a mathematical analysis demonstrating that transformers cannot achieve general predictive accuracy due to inherent architectural limitations. |
| title | Analyzing limits for in-context learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.03503 |