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parameter-efficient fine-tuning

Parameter-Efficient Fine-Tuning (PEFT) is a technique used to adapt pre-trained models to new tasks with minimal changes to the model's parameters. This approach is particularly useful in scenarios where computational resources are limited or when it is desirable to maintain the original model's performance on the initial task.

Papers

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Showing 301–325 of 935 papers

TitleStatusHype
Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative TradingCode2
Towards Efficient Model-Heterogeneity Federated Learning for Large Models—0
Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning—0
LoRA-Mini : Adaptation Matrices Decomposition and Selective Training—0
Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models—0
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement—0
Exploring Foundation Models Fine-Tuning for Cytology ClassificationCode1
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation—0
Visual Cue Enhancement and Dual Low-Rank Adaptation for Efficient Visual Instruction Fine-Tuning—0
F^3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization—0
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model—0
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks—0
MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical MapsCode1
Prompt-Efficient Fine-Tuning for GPT-like Deep Models to Reduce Hallucination and to Improve Reproducibility in Scientific Text Generation Using Stochastic Optimisation Techniques—0
CULL-MT: Compression Using Language and Layer pruning for Machine Translation—0
Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank AdaptationCode0
MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for MambaCode1
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation—0
Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study—0
Expanding Sparse Tuning for Low Memory UsageCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
Is Multiple Object Tracking a Matter of Specialization?—0
Dual Low-Rank Adaptation for Continual Learning with Pre-Trained Models—0
CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning—0
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All datasetsBoolQHellaSwagWinoGrande

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )82.63—Unverified
2LLaMA2-7bAccuracy (% )82.63—Unverified
3LLaMA2-7bAccuracy (% )81.93—Unverified
4LLaMA2-7bAccuracy (% )80.28—Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )76.68—Unverified
2LLaMA2-7bAccuracy (% )76.67—Unverified
3LLaMA2-7bAccuracy (% )76.27—Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )70.8—Unverified
2LLaMA2-7bAccuracy (% )70.09—Unverified
3LLaMA2-7bAccuracy (% )69.85—Unverified