parameter-efficient fine-tuning
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Benchmarks
Most implemented
LoRA: Low-Rank Adaptation of Large Language Models
QLoRA: Efficient Finetuning of Quantized LLMs
Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
DoRA: Weight-Decomposed Low-Rank Adaptation
Papers
Task-Oriented Rank Adaptation for Continual Learning in Text Classification
Continual learning (CL) in text classification faces two critical challenges: catastrophic forgetting and negative transfer across sequential tasks. Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA enable effi…
parameter-efficient fine-tuningText ClassificationContinual LearningBeyond Domain-Level Adaptation: Margin-Oriented Semantic-Appearance Interaction Correction for Personalized Federated Vision-Language Models
Federated parameter-efficient fine-tuning enables distributed clients to adapt pretrained vision-language models without sharing raw data or updating the full backbone. Its effectiveness, however, is limited by domain he…
parameter-efficient fine-tuningMetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes
We introduce MetroLLM-Bench, a 955-case benchmark for testing language models as the policy layer of a transit kiosk. It covers six real metro systems, ranging from 37 to 414 stations, and eleven categories that include …
parameter-efficient fine-tuning5-Dialects-BN: Unmasking the Impact of Transliteration on Bangla Dialectal LLMs
Large Language Models (LLMs) have achieved remarkable progress across natural language processing (NLP) tasks, yet their capabilities degrade sharply for low-resource languages and dialectally diverse settings. Bangla, t…
parameter-efficient fine-tuningMachine TranslationViBe: Visual Behavior Adaptation for Perceptive Humanoid Whole-Body Control
Motion tracking provides a scalable recipe for humanoid whole-body control. By design, the resulting trackers lack exteroceptive feedback hence reacting to the environment remains the responsibility of a higher-level pla…
parameter-efficient fine-tuningPRICE: A Systematic Study of LLM Adaptation Choices for Bitcoin Price Forecasting
Cryptocurrency markets exhibit extreme volatility and non-stationary dynamics that challenge conventional forecasting methods. Although Large Language Models (LLMs) have shown promise for time series forecasting, the com…
parameter-efficient fine-tuningTime Series Forecasting