@inproceedings{nguyen-etal-2025-task,
title = "Task-driven Layerwise Additive Activation Intervention",
author = "Nguyen, Hieu Trung and
Nguyen, Bao and
Nguyen, Binh and
Nguyen, Viet Anh",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-short.43/",
doi = "10.18653/v1/2025.naacl-short.43",
pages = "506--513",
ISBN = "979-8-89176-190-2",
abstract = "Modern language models (LMs) have significantly advanced generative modeling in natural language processing (NLP). Despite their success, LMs often struggle with adaptation to new contexts in real-time applications. A promising approach to task adaptation is activation intervention, which steers the LMs' generation process by identifying and manipulating the activations. However, existing interventions rely heavily on heuristic rules or require many prompt inputs to determine effective interventions. In this paper, we propose a layer-wise additive activation intervention framework that optimizes the intervention process, thereby enhancing sample efficiency. We evaluate our framework on various datasets, demonstrating improvements in the accuracy of pretrained LMs and competing intervention baselines."
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%0 Conference Proceedings
%T Task-driven Layerwise Additive Activation Intervention
%A Nguyen, Hieu Trung
%A Nguyen, Bao
%A Nguyen, Binh
%A Nguyen, Viet Anh
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-190-2
%F nguyen-etal-2025-task
%X Modern language models (LMs) have significantly advanced generative modeling in natural language processing (NLP). Despite their success, LMs often struggle with adaptation to new contexts in real-time applications. A promising approach to task adaptation is activation intervention, which steers the LMs’ generation process by identifying and manipulating the activations. However, existing interventions rely heavily on heuristic rules or require many prompt inputs to determine effective interventions. In this paper, we propose a layer-wise additive activation intervention framework that optimizes the intervention process, thereby enhancing sample efficiency. We evaluate our framework on various datasets, demonstrating improvements in the accuracy of pretrained LMs and competing intervention baselines.
%R 10.18653/v1/2025.naacl-short.43
%U https://aclanthology.org/2025.naacl-short.43/
%U https://doi.org/10.18653/v1/2025.naacl-short.43
%P 506-513
Markdown (Informal)
[Task-driven Layerwise Additive Activation Intervention](https://aclanthology.org/2025.naacl-short.43/) (Nguyen et al., NAACL 2025)
ACL
- Hieu Trung Nguyen, Bao Nguyen, Binh Nguyen, and Viet Anh Nguyen. 2025. Task-driven Layerwise Additive Activation Intervention. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 506–513, Albuquerque, New Mexico. Association for Computational Linguistics.