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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/2501.09240 |
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| _version_ | 1866915105325711360 |
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| author | Yang, Liu Lin, Ziqian Lee, Kangwook Papailiopoulos, Dimitris Nowak, Robert |
| author_facet | Yang, Liu Lin, Ziqian Lee, Kangwook Papailiopoulos, Dimitris Nowak, Robert |
| contents | In-context learning is a remarkable capability of transformers, referring to their ability to adapt to specific tasks based on a short history or context. Previous research has found that task-specific information is locally encoded within models, though their emergence and functionality remain unclear due to opaque pre-training processes. In this work, we investigate the formation of task vectors in a controlled setting, using models trained from scratch on synthetic datasets. Our findings confirm that task vectors naturally emerge under certain conditions, but the tasks may be relatively weakly and/or non-locally encoded within the model. To promote strong task vectors encoded at a prescribed location within the model, we propose an auxiliary training mechanism based on a task vector prompting loss (TVP-loss). This method eliminates the need to search for task-correlated encodings within the trained model and demonstrably improves robustness and generalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09240 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Task Vectors in In-Context Learning: Emergence, Formation, and Benefit Yang, Liu Lin, Ziqian Lee, Kangwook Papailiopoulos, Dimitris Nowak, Robert Machine Learning In-context learning is a remarkable capability of transformers, referring to their ability to adapt to specific tasks based on a short history or context. Previous research has found that task-specific information is locally encoded within models, though their emergence and functionality remain unclear due to opaque pre-training processes. In this work, we investigate the formation of task vectors in a controlled setting, using models trained from scratch on synthetic datasets. Our findings confirm that task vectors naturally emerge under certain conditions, but the tasks may be relatively weakly and/or non-locally encoded within the model. To promote strong task vectors encoded at a prescribed location within the model, we propose an auxiliary training mechanism based on a task vector prompting loss (TVP-loss). This method eliminates the need to search for task-correlated encodings within the trained model and demonstrably improves robustness and generalization. |
| title | Task Vectors in In-Context Learning: Emergence, Formation, and Benefit |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.09240 |