| Challenge: | Parameter-Efficient Fine-tuning (PEFT) methods are limited on knowledge-intensive tasks due to the limited number of trainable parameters. |
| Approach: | They propose a mechanism that fine-tunes Large Language Models with larger adapters . they store and update the parameters of larger adapter adapters on the CPU . |
| Outcome: | The proposed method achieves comparable results to those obtained with larger memory capacities over the limited bandwidth of PCI Express (PCIe). |
Similar Papers
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models (2023.emnlp-main)
Copied to clipboard
Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, Roy Lee
| Challenge: | Large language models (LLMs) have shown unprecedented performance across various tasks. |
| Approach: | They propose an easy-to-use framework that integrates adapters into LLMs . they evaluate adapters on 14 datasets from two different reasoning tasks . |
| Outcome: | The proposed framework can be used to fine-tune open-access language models with task-specific data and instruction data. |
Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on parameter-efficient fine-tuning (PEFT) for dense-architecture LLMs are lacking. |
| Approach: | They propose an expert-specialized fine-tuning method that tunes the experts most relevant to downstream tasks while freezing the other experts. |
| Outcome: | The proposed method matches or surpasses full-parameter fine-tuning. |
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation (2024.findings-naacl)
Copied to clipboard
| Challenge: | Parameter-efficient fine-tuning (PEFT) methods are important in low-resource language (LRL) Neural Machine Translation (NMT) but their practical effectiveness varies significantly across different languages. |
| Approach: | They evaluated the performance of 8 parameters-efficient fine-tuning methods with 15 architectures using the SacreBLEU score. |
| Outcome: | The Houlsby+Inversion adapter outperforms the baseline architectures in both in-domain and out-domain tests and the Houlson+Inverter achieves the best performance overall. |
PEFT-Factory: Unified Parameter-Efficient Fine-Tuning of Autoregressive Large Language Models (2026.eacl-demo)
Copied to clipboard
| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) methods address the increasing size of Large Language Models (LLMs). |
| Approach: | They propose a framework for efficient fine-tuning Large Language Models (LLMs) they aim to train only a small percentage of the full model's parameters . |
| Outcome: | Xu et al., 2023; Ding e t al, 2024; Lialin e al. 2023) show that using PEFT methods can improve performance. |
PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark (2026.eacl-long)
Copied to clipboard
| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) methods reduce the number of trainable parameters while maintaining strong downstream performance. |
| Approach: | They propose a unified benchmark for evaluating diverse PEFT methods on autoregressive LLMs. |
| Outcome: | The proposed methods reduce trainable parameters while maintaining strong downstream performance. |
Parameter-Efficient Fine-Tuning without Introducing New Latency (2023.acl-long)
Copied to clipboard
| Challenge: | Parameter-efficient fine-tuning of pre-trained language models has been demonstrated to be effective, but its inherent characteristics limit its performance. |
| Approach: | They propose to generate a sparse mask in a task-agnostic manner by modifying only a small subset of existing parameters and adding new parameters. |
| Outcome: | The proposed method surpasses existing methods on the GLUE benchmark by a significant margin. |
Sparsity May Be All You Need: Sparse Random Parameter Adaptation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Parameter-Efficient Fine-Tuning (PEFT) methods aim at reducing computational and memory resources for fine-tuning large language models. |
| Approach: | They propose to train on a small number of parameters instead of all model parameters . they compare the method to LoRA and find it to be efficient . |
| Outcome: | The proposed method is competitive with LoRA when using a similar number of trainable parameters. |
When does Parameter-Efficient Transfer Learning Work for Machine Translation? (2022.emnlp-main)
Copied to clipboard
| Challenge: | Prior work indicates that parametric fine-tuning methods may not work as well for machine translation (MT). |
| Approach: | They propose to use parameter-efficient fine-tuning methods to adapt large pre-trained models while only tuning a small number of parameters. |
| Outcome: | The proposed methods outperform full fine-tuning for many downstream tasks when the parameter budget corresponds to 10% of the model parameters. |
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings (2023.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) have ignited a surge in demand for effective fine-tuning techniques . data labeling is notoriously time-consuming and expensive, hindering the development of sizable labeled datasets . |
| Approach: | They propose to use active learning to reduce labeling costs by minimizing label complexity . they find PEFT adapter modules have significant potential in low-resource settings . |
| Outcome: | The proposed model outperforms FFT in low-resource settings and shows that it yields more stable representations of early and middle layers than FFT. |
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning large language models are inefficient and redundant . a light-PEFT framework can be used to prune redundant parameters during training . |
| Approach: | They propose a parameter-efficient fine-tuning framework that freezes most parameters of the foundation model and finetuns only a small number of parameters. |
| Outcome: | The proposed framework achieves training and inference speedup, reduces memory usage, and maintains comparable performance and plug-and-play feature of PEFT. |