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    <abstract>Large language models perform well on tasks that have undergone fine-tuning of instructions, but their performance on completely unseen tasks is often less than ideal. To overcome the challenge of cross-task generalization, task-level LoRAs combination is proposed, which does not require training a model for new tasks. Instead, it learns the LoRA modules combination weights based on a small number of samples to form the task model. However, task-level LoRAs combination only utilizes a few task modules due to its reliance on the weight enumeration method, and it also ignores the specificity between different instances. Therefore, we proposed an instance-level LoRAs composition for cross-task generalization, which selects appropriate multiple task LoRA modules for each input instance and dynamically determines the composition weights. Our experiments on publicly available datasets show that our method outperforms the typical method, LoraHub, in 16 out of 27 tasks. We release the source code at https://github.com/noname822/iLoraComp.git</abstract>
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%0 Conference Proceedings
%T Instance-Level Dynamic LoRAs Composition for Cross-Task Generalization
%A Wang, Zhiqi
%A He, Shizhu
%A Liu, Kang
%A Zhao, Jun
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wang-etal-2024-instance
%X Large language models perform well on tasks that have undergone fine-tuning of instructions, but their performance on completely unseen tasks is often less than ideal. To overcome the challenge of cross-task generalization, task-level LoRAs combination is proposed, which does not require training a model for new tasks. Instead, it learns the LoRA modules combination weights based on a small number of samples to form the task model. However, task-level LoRAs combination only utilizes a few task modules due to its reliance on the weight enumeration method, and it also ignores the specificity between different instances. Therefore, we proposed an instance-level LoRAs composition for cross-task generalization, which selects appropriate multiple task LoRA modules for each input instance and dynamically determines the composition weights. Our experiments on publicly available datasets show that our method outperforms the typical method, LoraHub, in 16 out of 27 tasks. We release the source code at https://github.com/noname822/iLoraComp.git
%U https://aclanthology.org/2024.findings-emnlp.326
%P 5699-5708

Markdown (Informal)

[Instance-Level Dynamic LoRAs Composition for Cross-Task Generalization](https://aclanthology.org/2024.findings-emnlp.326) (Wang et al., Findings 2024)

• Instance-Level Dynamic LoRAs Composition for Cross-Task Generalization (Wang et al., Findings 2024)

ACL

• Zhiqi Wang, Shizhu He, Kang Liu, and Jun Zhao. 2024. Instance-Level Dynamic LoRAs Composition for Cross-Task Generalization. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5699–5708, Miami, Florida, USA. Association for Computational Linguistics.