@inproceedings{tavares-etal-2025-language,
title = "Language Models Can be Efficiently Steered via Minimal Embedding Layer Transformations",
author = "Tavares, Diogo and
Semedo, David and
Rudnicky, Alexander and
Magalhaes, Joao",
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.1170/",
pages = "22960--22978",
ISBN = "979-8-89176-332-6",
abstract = "Large Language Models (LLMs) are increasingly costly to fine-tune due to their size, with embedding layers alone accounting for up to 20{\%} of model parameters. While Parameter-Efficient Fine-Tuning (PEFT) methods exist, they largely overlook the embedding layer. In this paper, we introduce TinyTE, a novel PEFT approach that steers model behavior via minimal translational transformations in the embedding space. TinyTE modifies input embeddings without altering hidden layers, achieving competitive performance while requiring approximately 0.0001{\%} of the parameters needed for full fine-tuning. Experiments across architectures provide a new lens for understanding the relationship between input representations and model behavior{---}revealing them to be more flexible at their foundation than previously thought."
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<abstract>Large Language Models (LLMs) are increasingly costly to fine-tune due to their size, with embedding layers alone accounting for up to 20% of model parameters. While Parameter-Efficient Fine-Tuning (PEFT) methods exist, they largely overlook the embedding layer. In this paper, we introduce TinyTE, a novel PEFT approach that steers model behavior via minimal translational transformations in the embedding space. TinyTE modifies input embeddings without altering hidden layers, achieving competitive performance while requiring approximately 0.0001% of the parameters needed for full fine-tuning. Experiments across architectures provide a new lens for understanding the relationship between input representations and model behavior—revealing them to be more flexible at their foundation than previously thought.</abstract>
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%0 Conference Proceedings
%T Language Models Can be Efficiently Steered via Minimal Embedding Layer Transformations
%A Tavares, Diogo
%A Semedo, David
%A Rudnicky, Alexander
%A Magalhaes, Joao
%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 tavares-etal-2025-language
%X Large Language Models (LLMs) are increasingly costly to fine-tune due to their size, with embedding layers alone accounting for up to 20% of model parameters. While Parameter-Efficient Fine-Tuning (PEFT) methods exist, they largely overlook the embedding layer. In this paper, we introduce TinyTE, a novel PEFT approach that steers model behavior via minimal translational transformations in the embedding space. TinyTE modifies input embeddings without altering hidden layers, achieving competitive performance while requiring approximately 0.0001% of the parameters needed for full fine-tuning. Experiments across architectures provide a new lens for understanding the relationship between input representations and model behavior—revealing them to be more flexible at their foundation than previously thought.
%U https://aclanthology.org/2025.emnlp-main.1170/
%P 22960-22978
Markdown (Informal)
[Language Models Can be Efficiently Steered via Minimal Embedding Layer Transformations](https://aclanthology.org/2025.emnlp-main.1170/) (Tavares et al., EMNLP 2025)
ACL