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 651–675 of 935 papers

TitleStatusHype
SwitchLoRA: Switched Low-Rank Adaptation Can Learn Full-Rank Information—0
Risks When Sharing LoRA Fine-Tuned Diffusion Model Weights—0
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA—0
Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA—0
RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts—0
RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarization—0
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning—0
SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation—0
SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation—0
SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction—0
Scaled Prompt-Tuning for Few-Shot Natural Language Generation—0
Scaling Laws for Forgetting When Fine-Tuning Large Language Models—0
Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization—0
SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA—0
Sculpting [CLS] Features for Pre-Trained Model-Based Class-Incremental Learning—0
SECURA: Sigmoid-Enhanced CUR Decomposition with Uninterrupted Retention and Low-Rank Adaptation in Large Language Models—0
Selective Fine-tuning on LLM-labeled Data May Reduce Reliance on Human Annotation: A Case Study Using Schedule-of-Event Table Detection—0
Self-Corrected Multimodal Large Language Model for End-to-End Robot Manipulation—0
Semantic are Beacons: A Semantic Perspective for Unveiling Parameter-Efficient Fine-Tuning in Knowledge Learning—0
Sensitivity-Aware Efficient Fine-Tuning via Compact Dynamic-Rank Adaptation—0
Sequential Compression Layers for Efficient Federated Learning in Foundational Models—0
Sequential LLM Framework for Fashion Recommendation—0
Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters—0
Shears: Unstructured Sparsity with Neural Low-rank Adapter Search—0
SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning—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