Papers parameter-efficient fine-tuning
“parameter-efficient fine-tuning” 태그가 달린 논문 1,616편 · 필터 해제
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 ForecastingReading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training
Large language models are increasingly capable in general, but their utility can remain modest in niche or understudied areas. One approach to address this limitation is to specialise existing models through additional t…
parameter-efficient fine-tuningInstruction FollowingD-TAIA: Domain-Aware LLM Adaptation for Multi-Task Predictive Process Monitoring
Predictive Process Monitoring (PPM) enables organizations to forecast future process behavior, such as the next activity and remaining time of ongoing cases. In practice, three conditions cause existing methods to degrad…
parameter-efficient fine-tuningToken-Budget Distillation: Transferring Full-Token Semantics to Compressed Video Vision-Language Models
Adapting video vision-language models (VLMs) is computationally expensive because video inputs produce a large number of visual tokens, making both fine-tuning and inference costly. Although visual token compression can …
parameter-efficient fine-tuningFISGuard: Defending Against Membership Inference via Fixed Input Subspaces
As large language models are increasingly adopted in federated learning, protecting user privacy while performing parameter-efficient fine-tuning on distributed private data has become an important challenge. Although cl…
parameter-efficient fine-tuningFederated LearningParameter Efficient Continual Learning for Sparse Event-Based Transformers
Robotic and edge intelligence systems operate in dynamic environments where data arrives continuously, requiring models to adapt while preserving previously learned knowledge under strict memory and energy constraints. W…
parameter-efficient fine-tuningclass-incremental learningContinual LearningEvent-based visionLearning New Facts with QLoRA: An Acquisition-Retention Frontier
Parameter-efficient fine-tuning is often assumed to preserve pretrained capabilities because it updates only a small number of parameters. We show that this assumption depends strongly on adapter capacity. We study factu…
parameter-efficient fine-tuningMAMA-FLUX.2: Image-to-Image Synthesis of Post-Contrast Breast DCE-MRI for the MAMA-SYNTH Challenge
Dynamic contrast-enhanced breast MRI is central to cancer diagnosis and monitoring, but requires gadolinium-based contrast agents. In this work, we address pre-to-post contrast breast MRI synthesis for the MAMA-SYNTH cha…
parameter-efficient fine-tuningCrossMambaTuning: Synergistic Spatial and Cross-Layer Adaptation for Machine Vision Compression
To reduce deployment cost and retraining overhead, adapting pretrained learned image compression (LIC) models to downstream machine vision tasks has attracted growing attention. However, existing methods typically insert…
parameter-efficient fine-tuningImage CompressionVision-Language Models for Analog Gauge Reading: An Empirical Study of Specialization, Transfer and Reliability
Analog gauges remain common in industrial environments where manual inspection is costly or hazardous. The engineering application addressed here is direct numerical reading of single-target analog-gauge images, while th…
parameter-efficient fine-tuningSelf-Routed Tensor Adapters for Parameter-Efficient Universal Visual Adaptation
Universal visual representations require adaptation mechanisms that adapt across heterogeneous domains without fragmenting knowledge into domain-specific modules. Parameter-efficient fine-tuning adapts frozen visual foun…
parameter-efficient fine-tuningDomain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation
Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpretability often degrades on specialized co…
parameter-efficient fine-tuningTopic ModelsApproximate Muon with low-rank adapters
The Muon optimizer shows clear benefits versus alternatives when pretraining neural networks. However, it is used less frequently for parameter-efficient fine-tuning (PEFT). One potential reason is that the most common P…
parameter-efficient fine-tuningBehavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds
Large language models (LLMs) are predominantly aligned to function as passive, sycophantic assistants. We challenge this default paradigm by empirically evaluating the cognitive plasticity of open-weight architectures wh…
parameter-efficient fine-tuningQuestion GenerationYOLO-PEFT: Parameter-Efficient Fine-Tuning on YOLO Family
Generic parameter-efficient fine-tuning (PEFT) methods transferred from language models can fail silently on real-time detectors, whose heterogeneous operators and detection-specific components impose placement constrain…
parameter-efficient fine-tuning