@inproceedings{zhao-etal-2025-sparse,
title = "Sparse Activation Editing for Reliable Instruction Following in Narratives",
author = "Zhao, Runcong and
Cao, Chengyu and
Zhu, Qinglin and
Ly, Xiucheng and
Shao, Shun and
Gui, Lin and
Xu, Ruifeng and
He, Yulan",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1311/",
doi = "10.18653/v1/2025.emnlp-main.1311",
pages = "25817--25832",
ISBN = "979-8-89176-332-6",
abstract = "Complex narrative contexts often challenge language models' ability to follow instructions, and existing benchmarks fail to capture these difficulties. To address this, we propose Concise-SAE, a training-free framework that improves instruction following by identifying and editing instruction-relevant neurons using only natural language instructions, without requiring labelled data. To thoroughly evaluate our method, we introduce FreeInstruct, a diverse and realistic benchmark that highlights the challenges of instruction following in narrative-rich settings. While initially motivated by complex narratives, Concise-SAE demonstrates state-of-the-art instruction adherence across varied tasks without compromising generation quality."
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<abstract>Complex narrative contexts often challenge language models’ ability to follow instructions, and existing benchmarks fail to capture these difficulties. To address this, we propose Concise-SAE, a training-free framework that improves instruction following by identifying and editing instruction-relevant neurons using only natural language instructions, without requiring labelled data. To thoroughly evaluate our method, we introduce FreeInstruct, a diverse and realistic benchmark that highlights the challenges of instruction following in narrative-rich settings. While initially motivated by complex narratives, Concise-SAE demonstrates state-of-the-art instruction adherence across varied tasks without compromising generation quality.</abstract>
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%0 Conference Proceedings
%T Sparse Activation Editing for Reliable Instruction Following in Narratives
%A Zhao, Runcong
%A Cao, Chengyu
%A Zhu, Qinglin
%A Ly, Xiucheng
%A Shao, Shun
%A Gui, Lin
%A Xu, Ruifeng
%A He, Yulan
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhao-etal-2025-sparse
%X Complex narrative contexts often challenge language models’ ability to follow instructions, and existing benchmarks fail to capture these difficulties. To address this, we propose Concise-SAE, a training-free framework that improves instruction following by identifying and editing instruction-relevant neurons using only natural language instructions, without requiring labelled data. To thoroughly evaluate our method, we introduce FreeInstruct, a diverse and realistic benchmark that highlights the challenges of instruction following in narrative-rich settings. While initially motivated by complex narratives, Concise-SAE demonstrates state-of-the-art instruction adherence across varied tasks without compromising generation quality.
%R 10.18653/v1/2025.emnlp-main.1311
%U https://aclanthology.org/2025.emnlp-main.1311/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1311
%P 25817-25832
Markdown (Informal)
[Sparse Activation Editing for Reliable Instruction Following in Narratives](https://aclanthology.org/2025.emnlp-main.1311/) (Zhao et al., EMNLP 2025)
ACL
- Runcong Zhao, Chengyu Cao, Qinglin Zhu, Xiucheng Ly, Shun Shao, Lin Gui, Ruifeng Xu, and Yulan He. 2025. Sparse Activation Editing for Reliable Instruction Following in Narratives. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 25817–25832, Suzhou, China. Association for Computational Linguistics.