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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 526–550 of 935 papers

TitleStatusHype
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
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks—0
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model—0
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
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
Dual Low-Rank Adaptation for Continual Learning with Pre-Trained Models—0
Is Multiple Object Tracking a Matter of Specialization?—0
CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning—0
Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding—0
EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection—0
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation—0
Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models—0
MALoRA: Mixture of Asymmetric Low-Rank Adaptation for Enhanced Multi-Task Learning—0
Meta-Learning Adaptable Foundation Models—0
Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion ModelsCode0
Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation—0
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs—0
GeoLoRA: Geometric integration for parameter efficient fine-tuning—0
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies—0
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning—0
FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation—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