CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models (2024.emnlp-main)
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| Challenge: | Existing multi-task learning approaches for large language models fall short due to computational intensive or lack of simultaneous task convergence. |
| Approach: | They propose a new multi-task learning approach that dynamically adjusts task weights during the training process, ensuring that the validation loss of all tasks progresses towards convergence at an even pace. |
| Outcome: | The proposed approach improves the performance of large language models by up to 13% compared to the second-best approaches. |
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