@inproceedings{chen-etal-2025-layer,
title = "Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge",
author = "Chen, Yan-Lun and
Wei, Yi-Ru and
Hsu, Chia-Yi and
Yu, Chia-Mu and
Huang, Chun-Ying and
Lin, Ying-Dar and
Wu, Yu-Sung and
Lee, Wei-Bin",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.644/",
doi = "10.18653/v1/2025.findings-emnlp.644",
pages = "12033--12054",
ISBN = "979-8-89176-335-7",
abstract = "Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine-tuned models often leads to degraded performance due to overlapping instruction-following components. Task Arithmetic (TA), which combines task vectors derived from fine-tuning, enables multi-task learning and task forgetting but struggles to isolate task-specific knowledge from general instruction-following behavior. To address this, we propose Layer-Aware Task Arithmetic (LATA), a novel approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. By amplifying task-relevant layers and attenuating instruction-following layers, LATA improves task learning and forgetting performance while preserving overall model utility. Experiments on multiple benchmarks, including WikiText-2, GSM8K, and HumanEval, demonstrate that LATA outperforms existing methods in both multi-task learning and selective task forgetting, achieving higher task accuracy and alignment with minimal degradation in output quality. Our findings highlight the importance of layer-wise analysis in disentangling task-specific and general-purpose knowledge, offering a robust framework for efficient model merging and editing."
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<abstract>Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine-tuned models often leads to degraded performance due to overlapping instruction-following components. Task Arithmetic (TA), which combines task vectors derived from fine-tuning, enables multi-task learning and task forgetting but struggles to isolate task-specific knowledge from general instruction-following behavior. To address this, we propose Layer-Aware Task Arithmetic (LATA), a novel approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. By amplifying task-relevant layers and attenuating instruction-following layers, LATA improves task learning and forgetting performance while preserving overall model utility. Experiments on multiple benchmarks, including WikiText-2, GSM8K, and HumanEval, demonstrate that LATA outperforms existing methods in both multi-task learning and selective task forgetting, achieving higher task accuracy and alignment with minimal degradation in output quality. Our findings highlight the importance of layer-wise analysis in disentangling task-specific and general-purpose knowledge, offering a robust framework for efficient model merging and editing.</abstract>
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%0 Conference Proceedings
%T Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge
%A Chen, Yan-Lun
%A Wei, Yi-Ru
%A Hsu, Chia-Yi
%A Yu, Chia-Mu
%A Huang, Chun-Ying
%A Lin, Ying-Dar
%A Wu, Yu-Sung
%A Lee, Wei-Bin
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F chen-etal-2025-layer
%X Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine-tuned models often leads to degraded performance due to overlapping instruction-following components. Task Arithmetic (TA), which combines task vectors derived from fine-tuning, enables multi-task learning and task forgetting but struggles to isolate task-specific knowledge from general instruction-following behavior. To address this, we propose Layer-Aware Task Arithmetic (LATA), a novel approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. By amplifying task-relevant layers and attenuating instruction-following layers, LATA improves task learning and forgetting performance while preserving overall model utility. Experiments on multiple benchmarks, including WikiText-2, GSM8K, and HumanEval, demonstrate that LATA outperforms existing methods in both multi-task learning and selective task forgetting, achieving higher task accuracy and alignment with minimal degradation in output quality. Our findings highlight the importance of layer-wise analysis in disentangling task-specific and general-purpose knowledge, offering a robust framework for efficient model merging and editing.
%R 10.18653/v1/2025.findings-emnlp.644
%U https://aclanthology.org/2025.findings-emnlp.644/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.644
%P 12033-12054
Markdown (Informal)
[Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge](https://aclanthology.org/2025.findings-emnlp.644/) (Chen et al., Findings 2025)
ACL
- Yan-Lun Chen, Yi-Ru Wei, Chia-Yi Hsu, Chia-Mu Yu, Chun-Ying Huang, Ying-Dar Lin, Yu-Sung Wu, and Wei-Bin Lee. 2025. Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 12033–12054, Suzhou, China. Association for Computational Linguistics.