Papers with Learnable Calibration
Efficient Compositional Multi-tasking for On-device Large Language Models (2025.emnlp-main)
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| Challenge: | Adapter parameters provide a mechanism to modify the behavior of machine learning models and have gained significant popularity in the context of large language models (LLMs). |
| Approach: | They propose a benchmark for text-based compositional multi-tasking where multiple tasks are executed simultaneously. |
| Outcome: | The proposed method is optimized for on-device applications where computational resources are limited. |