Aspect-based sentiment analysis (ABSA) plays a vital role in deriving fine-grained sentiment from textual content. As large language models (LLMs) are increasingly adopted for automated data annotation in natural language processing (NLP), concerns h...
With the growing scale of pre-trained language models (PLMs), full parameter fine-tuning becomes prohibitively expensive and practically infeasible. Therefore, parameter-efficient adaptation techniques for PLMs have been proposed to learn through inc...
Despite recent advances, medical question answering systems still struggle with domain-specific reasoning and data efficiency. This paper presents Med-LLaMA3, a family of medical large language models developed by parameter-efficient fine-tuning of t...
Distribution grids are essential to power systems, as they ensure the safe and reliable operation of power systems. It is important to forecast the operational status of each distribution transformer. Consequently, there is an urgent need to develop...
Pre-trained foundation models, trained on large-scale datasets, have demonstrated significant success in a variety of downstream vision tasks. Parameter-efficient fine-tuning (PEFT) methods aim to adapt these foundation models to new domains by updat...
Low-Rank Adaptation (LoRA) is a representative parameter-efficient fine-tuning method that reduces computational and memory costs without modifying the model architecture. Standard LoRA initializes matrix A from a symmetric distribution, such as Gaus...
Automated visual inspection of photovoltaic (PV) cells is an important component of solar-module quality assurance. However, adapting modern pre-trained vision backbones to PV defect classification remains challenging because full fine-tuning require...
Extracting road networks from Synthetic Aperture Radar (SAR) data represents a core challenge in remote sensing scene analysis, particularly for applications in traffic monitoring and emergency management. The task is complicated by several inherent...
Whisper is a transformer-based multilingual model that has illustrated state-of-the-art behavior in numerous languages. However, the efficiency remains persistent with the limited computational resources. To address this issue, an experiment was perf...
Recent studies on parameter-efficient fine-tuning (PEFT) have introduced effective and efficient methods for fine-tuning large language models (LLMs) on downstream tasks using fewer parameters than required by full fine-tuning. Low-rank decomposition...
CCTV safety monitoring demands anomaly detectors combine reliable clip-level accuracy with predictable per-clip latency despite weak supervision. This work investigates compact vision–language models (VLMs) as practical detectors for this regim...
Document-level Event Extraction (DEE) requires identifying complex event records and arguments dispersed across unstructured texts. However, applying general Large Language Models (LLMs) to DEE is intrinsically hindered by their lack of inductive bia...
Parameter-efficient fine-tuning (PEFT) reduces adaptation costs, but the predictive trade-offs may vary across datasets and encoder backbones. This study evaluated Hybrid Prefix–Prompt tuning for sentiment classification using BERT-base-uncased...
Federated learning lets multiple parties train a shared model without pooling their data, but a client with far less data than the others can end up poorly served even when the group’s average accuracy looks fine. We ask whether the choice of p...
Prompt tuning is a mainstream technique for fine-tuning large language models (LLMs), offering minimal parameter adjustments by learning task-specific prompt vectors. However, it suffers from training costs due to network-wide backpropagation and wea...
This study focuses on the neural machine translation task for the TR-EN language pair, which is considered a low-resource language pair. We investigated fine-tuning strategies for pre-trained language models. Specifically, we explored the effectivene...
Large language models (LLMs) have demonstrated strong potential in medical knowledge applications; however, their reliability in knowledge-intensive medical reasoning—remains limited due to hallucination, inadequate domain grounding, and unstab...
The digital transformation of oil and gas pipeline networks has generated substantial volumes of unstructured maintenance documentation from communication systems, creating an urgent need for automated summarization to improve operational efficiency....
This paper reports a systematic study of low-rank adaptation (LoRA)-based fine-tuning applied to Qwen3 language models (4B, 8B, and 14B parameters) for the task of legal question answering within the jurisdiction of the Republic of Kazakhstan. The bi...
Event cameras capture sparse, high-temporal-resolution visual information, making them attractive for challenging scenarios with fast motion and severe illumination changes. However, event-based depth models trained on one real-world benchmark often...
Source code plagiarism is a significant challenge in software engineering and computer science education, affecting academic integrity, intellectual property rights, and software quality assurance. However, Source Code Plagiarism Classification (SCPC...
Accurate rooftop segmentation from aerial imagery is essential for large-scale urban analysis, including applications such as solar potential assessment and urban monitoring. However, it remains constrained by the high cost of dense annotation and th...
The challenge of detecting nuanced, context-dependent offensive language highlights the need for Large Language Model (LLM) adaptation strategies that can effectively address inherent data and task asymmetries. Standard Parameter-Efficient Finetuning...
Accurate estimation of pasture biomass components from imagery is essential for sustainable grazing management and precision agriculture. Conventional methods such as destructive harvesting, rising plate meters, and remote sensing are limited by scal...
Automated CT lung nodule classification suffers from domain shift across centers. We propose LoRA-GRL, combining Low-Rank Adaptation (LoRA) with adversarial domain harmonization via a Gradient Reversal Layer. Rank-8 LoRA modules were inserted into al...
End-to-end voice cloning models like VITS require large amounts of data to produce high-quality speech, making speaker adaptation difficult when target data is limited. To address this problem, this paper introduces AdaptVITS, a parameter-efficient f...
Extreme summarization generates very short summaries, typically a single sentence, answering the question “What is the document about?”. Although large language models perform well in text generation, fine-tuning them for summarization of...
Promptable medical segmentation models can be sensitive to small targets, weak boundaries, heterogeneous texture, and inaccurate user prompts. We propose HBF-BCER, a prompt-preserving adaptation of the SAM ViT-B architecture for medical image segment...
Extremely low-resource machine translation poses a difficult cross-lingual adaptation problem: improving target-language performance while preserving capabilities acquired through multilingual pretraining. This study compares three mathematically dis...
Indian mythological narratives contain long event chains, recurring characters, moral conflicts, interactions between human and divine agents, and source-specific narrative styles. General-purpose large language models can generate fluent text while...
To address the trade-off between parameter scale and generation quality in Vision-Language Models (VLMs), this study proposes a Multi-Feature Dynamic Instruction Tuning (MFDIT) image captioning model based on LLaMA. By integrating CLIP-based global f...
Retrieval-augmented fine-tuning (RAFT) can improve small language models (sLMs) on retrieval-grounded question answering, but the synthetic training data produced by commercial large language models (LLMs) vary in quality. This paper contributes a qu...
Perception models for autonomous driving are predominantly trained on clear, daytime data, leaving their performance under rare conditions—particularly in multiple-attribute (joint weather–lighting) conditions such as night × rainy...
In low-resource automatic speech recognition (ASR) scenarios, parameter-efficient fine-tuning (PEFT) has become a crucial approach for adapting large pre-trained speech models. Although low-rank adaptation (LoRA) offers clear advantages in efficiency...
To address the triple bottlenecks of data scarcity, oversized models, and slow inference that hinder Cantonese automatic speech recognition (ASR) in low-resource and edge-deployment settings, this study proposes a cost-effective Cantonese ASR system...
This survey provides an in-depth review of large language models (LLMs), highlighting the significant paradigm shift they represent in artificial intelligence. Our purpose is to consolidate state-of-the-art advances in LLM design, training, adaptatio...
Remote sensing image segmentation is a foundational task in Earth observation. With the rapid growth of remote sensing datasets in terms of scale, modality diversity, semantic openness, and spatio-temporal complexity, the field is evolving from task-...
Legal judgment prediction (LJP) increasingly relies on large language models whose full fine-tuning is memory-intensive and susceptible to catastrophic forgetting. We present LawLLM-DS, a two-stage Low-Rank Adaptation (LoRA) framework that first perf...
Addressing the resource constraints of a single NVIDIA RTX 5000 (16 GB) GPU, this applied study takes DeepSeek-LLM-7B-Base as the base model and systematically compares four parameter-efficient fine-tuning methods: LoRA, QLoRA, DoRA, and LoRA+. It al...
Dynamic Facial Expression Recognition (DFER) has recently attracted significant interest due to its vital role in enabling empathetic and human-compatible technologies. Developing models that remain robust under in-the-wild variability is a key motiv...
(1) Background: Large language models (LLMs) show promise in healthcare but are constrained by hallucinations, static knowledge, and limited domain specificity. Fine-tuning (FT) and retrieval-augmented generation (RAG) offer complementary solutions,...
Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic–spatial inference fra...
Power load forecasting is critical for ensuring grid security and stability and optimizing energy resource allocation. The high integration of renewable energy poses significant challenges to traditional methods in data-scarce scenarios. Recently, La...
Satellite segmentation is vital for spacecraft perception, supporting tasks like structural analysis, fault detection, and in-orbit servicing. However, the generalization of existing methods is severely limited by the scarcity of target satellite dat...
High-quality text corpora are essential for knowledge graph construction and domain-specific large model pre-training in technology-intensive industries, with the steel metallurgy sector serving as a representative case. However, many industrial docu...
Adverse weather poses a major challenge to ground-based electro-optical (EO) aircraft surveillance. Existing parameter-efficient fine-tuning (PEFT) methods operate mainly in the spatial domain and treat weather as a generic domain shift rather than a...
In recent years, methods that selectively fine-tune or reduce the number of layers in large language models (LLMs) have garnered attention as an efficient alternative to traditional fine-tuning, where all layers are trained. In this study, we revisit...
In industrial scenarios, image segmentation is essential for accurately identifying defect regions. Recently, the emergence of foundation models driven by powerful computational resources and large-scale training data has brought about a paradigm shi...
Precise 3D reconstruction of non-cooperative space targets is a prerequisite for active debris removal and on-orbit servicing. However, this task is impeded by severe environmental challenges. Specifically, the limited dynamic range of visible light...
With the continuous advancement of deep neural networks, salient object detection (SOD) in natural images has made significant progress. However, SOD in optical remote sensing images (ORSI-SOD) remains a challenging task due to the diversity of objec...
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