Stochastic Fine-Tuning of Language Models Using Masked Gradients (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are the dominant paradigm in Natural Language Processing but fine-tuning them for specific downstream tasks often requires updating a vast number of parameters. |
| Approach: | They propose a method that selectively updates a small subset of parameters in each step of the tuning process. |
| Outcome: | The proposed approach outperforms existing fine-tuning methods while updating merely **0.08**% of the model’s parameters. |
Similar Papers
Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide (2026.tacl-1)
Copied to clipboard
| Challenge: | Pre-trained language models provide strong foundations, but effective adaptation under data scarcity requires efficient and efficient fine-tuning techniques. |
| Approach: | They propose to review parameter-efficient fine-tuning techniques that lower training and deployment costs and domain and cross-lingual adaptation methods for both encoder and decoder models. |
| Outcome: | The proposed techniques lower training and deployment costs, domain and cross-lingual adaptation methods, and model specialization strategies. |
Parameter-efficient Tuning for Large Language Model without Calculating Its Gradients (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent parameter-efficient tuning methods can only save 30% of training memory . gradient computation and backpropagation are still necessary for these methods . |
| Approach: | They propose a parameter-efficient tuning method that can be used to fine-tune large language models without calculating gradients. |
| Outcome: | The proposed method saves 30% of training memory and improves performance on large language models. |
Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER (2024.naacl-long)
Copied to clipboard
| Challenge: | Fine-tuning is the prevailing practice for adapting language models (LMs) to new domains. |
| Approach: | They propose a mask specific language model that weights the importance of domain-specific terms during fine-tuning to avoid insensitivity. |
| Outcome: | The proposed approach outperforms advanced masking strategies such as span- and PMI-based masking. |
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent pretrained language models extend from millions to billions of parameters. |
| Approach: | They propose a technique which forwards on a whole network while backwarding on resetting the gradients of the non-child network during the backward process. |
| Outcome: | The proposed technique outperforms the vanilla fine-tuning technique on various downstream tasks and can achieve better generalization performance by large margins. |
CBP-Tuning: Efficient Local Customization for Black-box Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Customized black-box prompt tuning is a new approach to customize large language models . however, as models grow, the resources required for training and deployment become increasingly expensive . |
| Approach: | They propose a framework that facilitates efficient local customization while preserving bidirectional privacy. |
| Outcome: | The proposed framework facilitates efficient local customization while preserving bidirectional privacy. |
PAC-tuning: Fine-tuning Pre-trained Language Models with PAC-driven Perturbed Gradient Descent (2023.emnlp-main)
Copied to clipboard
| Challenge: | PAC-tuning is a two-stage fine-tune method for pretrained language models . PAC training minimizes the PACBayes generalization bound to learn proper parameter distribution . |
| Approach: | They propose a two-stage fine-tuning method to minimize the PAC-Bayes generalization bound . they use PAC to inject noise with variance learned in the first stage into the model parameters . |
| Outcome: | The proposed method outperforms baseline methods on 5 GLUE benchmark tasks. |
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that powerful pre-trained language models can be fooled by small perturbations or intentional attacks. |
| Approach: | They propose a framework for fine-tuning PLMs using a masked language model and Gaussian noise to augment semantically relevant examples with sufficient diversity. |
| Outcome: | The proposed framework improves the robustness of pre-trained language models and alleviates performance degradation under adversarial attacks. |
Modular and Parameter-Efficient Fine-Tuning for NLP Models (2022.emnlp-tutorials)
Copied to clipboard
| Challenge: | State-of-the-art language models in NLP perform best when fine-tuned even on small datasets. |
| Approach: | They provide an overview of parameter-efficient fine-tuning methods and highlight similarities and differences . they highlight benefits and usage scenarios of a neglected property of parameter efficient models . |
| Outcome: | This paper provides an overview of parameter-efficient fine-tuning methods . it highlights similarities and differences by presenting them in a unified view . |
G-Tuning: Improving Generalization of Pre-trained Language Models with Generative Adversarial Network (2023.findings-acl)
Copied to clipboard
| Challenge: | Empirical evaluations on the GLUE benchmark demonstrate that fine-tuning can enhance the generalization performance of pre-trained language models (PLMs) in downstream tasks. |
| Approach: | They propose a fine-tuning framework that transforms the latent representation of pre-trained language models from a universal space to a target space and integrates a generative adversarial network into the fine-untun process. |
| Outcome: | Empirical evaluations on the GLUE benchmark and two additional demanding scenarios show that the proposed framework can improve the generalization performance of pre-trained language models (PLMs) in downstream tasks. |
Full Parameter Fine-tuning for Large Language Models with Limited Resources (2024.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) require massive GPU resources for training. |
| Approach: | They propose a parameter-efficient optimization that fuses the gradient computation and parameter update in one step to reduce memory usage. |
| Outcome: | The proposed method reduces memory usage to 10.8% compared to the standard approach. |