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

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Showing 551–575 of 935 papers

TitleStatusHype
Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates—0
BeamLoRA: Beam-Constraint Low-Rank Adaptation—0
Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models—0
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation—0
Bilevel ZOFO: Bridging Parameter-Efficient and Zeroth-Order Techniques for Efficient LLM Fine-Tuning and Meta-Training—0
BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning—0
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing—0
BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models—0
BiSup: Bidirectional Quantization Error Suppression for Large Language Models—0
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models—0
Black Sheep in the Herd: Playing with Spuriously Correlated Attributes for Vision-Language Recognition—0
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting—0
Block-wise LoRA: Revisiting Fine-grained LoRA for Effective Personalization and Stylization in Text-to-Image Generation—0
BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation—0
Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation—0
CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection—0
CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications—0
Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing—0
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion—0
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning—0
Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuning—0
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need—0
CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning—0
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment—0
CLLoRA: An Approach to Measure the Effects of the Context Length for LLM 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