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 701–725 of 935 papers

TitleStatusHype
HSACNet: Hierarchical Scale-Aware Consistency Regularized Semi-Supervised Change Detection—0
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models—0
HUT: A More Computation Efficient Fine-Tuning Method With Hadamard Updated Transformation—0
Hyper Compressed Fine-Tuning of Large Foundation Models with Quantum Inspired Adapters—0
HyperLoader: Integrating Hypernetwork-Based LoRA and Adapter Layers into Multi-Task Transformers for Sequence Labelling—0
Hypernetworks for Personalizing ASR to Atypical Speech—0
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks—0
HyperTuning: Toward Adapting Large Language Models without Back-propagation—0
IAPT: Instruction-Aware Prompt Tuning for Large Language Models—0
ICL Markup: Structuring In-Context Learning using Soft-Token Tags—0
iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation—0
Improving Domain Adaptation through Extended-Text Reading Comprehension—0
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning—0
Improving LoRA in Privacy-preserving Federated Learning—0
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective—0
InstantFT: An FPGA-Based Runtime Subsecond Fine-tuning of CNN Models—0
Investigating Automatic Scoring and Feedback using Large Language Models—0
Investigating Decoder-only Large Language Models for Speech-to-text Translation—0
Is Multiple Object Tracking a Matter of Specialization?—0
Is your LLM trapped in a Mental Set? Investigative study on how mental sets affect the reasoning capabilities of LLMs—0
iTBLS: A Dataset of Interactive Conversations Over Tabular Information—0
Keep the Balance: A Parameter-Efficient Symmetrical Framework for RGB+X Semantic Segmentation—0
KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning—0
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large 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