Results 11 to 20 of about 33,388 (259)
Lottery Rank-Pruning Adaptation Parameter Efficient Fine-Tuning
MathematicsRecent studies on parameter-efficient fine-tuning (PEFT) have introduced effective and efficient methods for fine-tuning large language models (LLMs) on downstream tasks using fewer parameters than required by full fine-tuning.
Juhyeong Kim, Gyunyeop Kim, Sangwoo Kang
doaj +2 more sources
Democratizing protein language models with parameter-efficient fine-tuning. [PDF]
Proc Natl Acad Sci U S A, 2023 AbstractProteomics has been revolutionized by large pre-trained protein language models, which learn unsupervised representations from large corpora of sequences. The parameters of these models are then fine-tuned in a supervised setting to tailor the model to a specific downstream task.
Sledzieski S +5 more
europepmc +4 more sources
Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 ICCV 2023 ...
He, Haoyu +4 more
openaire +2 more sources
Parameter-Efficient Fine-Tuning Design Spaces
, 2023 Code is available at https://github.com/amazon-science/peft-design ...
Chen, Jiaao +5 more
openaire +2 more sources
Parameter-Efficient Fine-Tuning without Introducing New Latency
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023 ACL2023 camera-ready ...
Liao, B., Meng, Y., Monz, C.
openaire +3 more sources
On the Effectiveness of Parameter-Efficient Fine-Tuning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always yields an entirely new model for each task. Currently, many research works propose to only fine-tune a small portion of the parameters while keeping most of the parameters ...
Fu, Zihao +5 more
openaire +2 more sources
Transactions of the Association for Computational LinguisticsAbstract Large pretrained language models are widely used in downstream NLP tasks via task- specific fine-tuning, but such procedures can be costly. Recently, Parameter-Efficient Fine-Tuning (PEFT) methods have achieved strong task performance while updating much fewer parameters than full model fine-tuning (FFT).
Han Zhou +3 more
doaj +3 more sources
CE-Prompt: enhance prompt expression stability by multiple understanding [PDF]
PeerJ Computer ScienceIn this article, we propose CE-Prompt, an enhanced version of Prompt-Tuning designed to address issues such as the instability of random initialization and inefficiencies caused by long text in pre-trained large language models (LLMs).
Wujian Yang +3 more
doaj +2 more sources
Exploring The Principles and Prospects for Efficient Fine-Tuning of Transformer-Based Pre-Trained Large Language Models [PDF]
ITM Web of ConferencesIn recent years, large language models (LLMs) have made breakthroughs in natural language processing and multimodal tasks. However, the growing model size and the high cost of full parameter fine-tuning pose challenges to their efficient adaptation. This
He Ruiqi
doaj +1 more source
TRACE: Time Series Parameter Efficient Fine-Tuning
NeurocomputingWe propose an efficient fine-tuning method for time series foundation models, termed TRACE: Time Series Parameter Efficient Fine-tuning. While pretrained time series foundation models are gaining popularity, they face the following challenges: (1) Unlike natural language tasks, time series data vary in frequency, channel numbers, historical/prediction ...
Yuze Li, Wei Zhu
openaire +2 more sources