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 526–550 of 935 papers

TitleStatusHype
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
LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM—0
Language and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization—0
LayerNorm: A key component in parameter-efficient fine-tuning—0
Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models—0
LeMo: Enabling LEss Token Involvement for MOre Context Fine-tuning—0
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs—0
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model—0
Lifelong Learning with Task-Specific Adaptation: Addressing the Stability-Plasticity Dilemma—0
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning—0
LLMI3D: Empowering LLM with 3D Perception from a Single 2D Image—0
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning—0
LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning—0
LoKi: Low-dimensional KAN for Efficient Fine-tuning Image Models—0
LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large 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