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 251–275 of 935 papers

TitleStatusHype
ABBA: Highly Expressive Hadamard Product Adaptation for Large Language ModelsCode1
Expanding Sparse Tuning for Low Memory UsageCode1
Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank StructuresCode1
CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental LearningCode1
FLoRA: Low-Rank Core Space for N-dimensionCode1
Harnessing Large Language Models for Text-Rich Sequential RecommendationCode1
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFTCode1
LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical ReportCode1
MeteoRA: Multiple-tasks Embedded LoRA for Large Language ModelsCode1
LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-TuningCode1
Exploring Foundation Models Fine-Tuning for Cytology ClassificationCode1
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language ModelsCode1
Exact and Efficient Unlearning for Large Language Model-based Recommendation—0
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need—0
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning—0
An Empirical Study on the Transferability of Transformer Modules in Parameter-Efficient Fine-Tuning—0
Enhancing News Summarization with ELearnFit through Efficient In-Context Learning and Efficient Fine-Tuning—0
Enhancing Multi-modal Models with Heterogeneous MoE Adapters for Fine-tuning—0
Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuning—0
Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter—0
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data—0
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning—0
GP-MoLFormer: A Foundation Model For Molecular Generation—0
Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability—0
Enhancing knowledge retention for continual learning with domain-specific adapters and features gating—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