SOTAVerified

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 401–450 of 935 papers

TitleStatusHype
LoRACode: LoRA Adapters for Code Embeddings—0
Personalized Text Generation with Contrastive Activation Steering—0
Addressing Overprescribing Challenges: Fine-Tuning Large Language Models for Medication Recommendation TasksCode0
Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA—0
PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation—0
Re-Imagining Multimodal Instruction Tuning: A Representation ViewCode0
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM—0
CLLoRA: An Approach to Measure the Effects of the Context Length for LLM Fine-Tuning—0
SECURA: Sigmoid-Enhanced CUR Decomposition with Uninterrupted Retention and Low-Rank Adaptation in Large Language Models—0
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models—0
R^3Mem: Bridging Memory Retention and Retrieval via Reversible Compression—0
Sparsity May Be All You Need: Sparse Random Parameter AdaptationCode0
NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language ModelsCode0
Sculpting [CLS] Features for Pre-Trained Model-Based Class-Incremental Learning—0
LoRA-GGPO: Mitigating Double Descent in LoRA Fine-Tuning via Gradient-Guided Perturbation OptimizationCode0
Generative Modeling of Individual Behavior at Scale—0
Black Sheep in the Herd: Playing with Spuriously Correlated Attributes for Vision-Language Recognition—0
LSR-Adapt: Ultra-Efficient Parameter Tuning with Matrix Low Separation Rank Kernel Adaptation—0
BeamLoRA: Beam-Constraint Low-Rank Adaptation—0
Token Adaptation via Side Graph Convolution for Temporally and Spatially Efficient Fine-tuning of 3D Point Cloud TransformersCode0
GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning—0
Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning Large Language Models—0
Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRACode0
Mitigating Visual Knowledge Forgetting in MLLM Instruction-tuning via Modality-decoupled Gradient Descent—0
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading—0
CiteCheck: Towards Accurate Citation Faithfulness DetectionCode0
Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA—0
DiffoRA: Enabling Parameter-Efficient LLM Fine-Tuning via Differential Low-Rank Matrix Adaptation—0
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits—0
MoLoRec: A Generalizable and Efficient Framework for LLM-Based Recommendation—0
Music for All: Representational Bias and Cross-Cultural Adaptability of Music Generation ModelsCode0
Hyper Compressed Fine-Tuning of Large Foundation Models with Quantum Inspired Adapters—0
Model Diffusion for Certifiable Few-shot Transfer Learning—0
ULPT: Prompt Tuning with Ultra-Low-Dimensional Optimization—0
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning—0
Bilevel ZOFO: Bridging Parameter-Efficient and Zeroth-Order Techniques for Efficient LLM Fine-Tuning and Meta-Training—0
FedP^2EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs—0
RandLoRA: Full-rank parameter-efficient fine-tuning of large models—0
Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA—0
Norm-Bounded Low-Rank Adaptation—0
Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability—0
High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2—0
LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation—0
LoRAGuard: An Effective Black-box Watermarking Approach for LoRAs—0
Decentralized Low-Rank Fine-Tuning of Large Language Models—0
Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation—0
Speech Translation Refinement using Large Language ModelsCode0
Complementary Subspace Low-Rank Adaptation of Vision-Language Models for Few-Shot Classification—0
Domain Expansion: Parameter-Efficient Modules as Building Blocks for Composite DomainsCode0
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models—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