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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 676–700 of 935 papers

TitleStatusHype
Asymmetry in Low-Rank Adapters of Foundation ModelsCode1
PeriodicLoRA: Breaking the Low-Rank Bottleneck in LoRA Optimization—0
Multimodal Instruction Tuning with Conditional Mixture of LoRACode1
Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning—0
Does Combining Parameter-efficient Modules Improve Few-shot Transfer Accuracy?—0
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning—0
Advancing Parameter Efficiency in Fine-tuning via Representation EditingCode1
Two-stage Cytopathological Image Synthesis for Augmenting Cervical Abnormality Screening—0
KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text DetectionCode1
MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models—0
LoRA Training in the NTK Regime has No Spurious Local MinimaCode0
SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning—0
NOTE: Notable generation Of patient Text summaries through Efficient approach based on direct preference optimization—0
Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic ForgettingCode0
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning—0
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources—0
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language ModelsCode1
GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural NetworkCode0
TuneTables: Context Optimization for Scalable Prior-Data Fitted NetworksCode1
Generalizability of Mixture of Domain-Specific Adapters from the Lens of Signed Weight Directions and its Application to Effective Model Pruning—0
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks—0
UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language ModelsCode1
Quantified Task Misalignment to Inform PEFT: An Exploration of Domain Generalization and Catastrophic Forgetting in CLIP—0
DoRA: Weight-Decomposed Low-Rank AdaptationCode4
An Embarrassingly Simple Approach for LLM with Strong ASR CapacityCode2
Show:102550
← PrevPage 28 of 38Next →
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