Cross-domain dialogue state tracking (DST) focuses on using labeled data from source domains to train a DST model for target domains. It is of great significance for transferring a dialogue system into new domains. Most of the existing cross-domain D...
With the launch of OpenAI’s ChatGPT, large language models have garnered significant attention, and applications based on these models have proliferated. A critical challenge has emerged: how to rapidly enhance the capabilities of general LLMs...
Text classification aims to classify text according to pre-defined categories. Despite the success of existing methods based on the fine-tuning paradigm, there is a significant gap between fine-tuning and pre-training. Currently, prompt learning meth...
In response to the significant dependency on empirical judgment in measuring the prompt neutron decay constant with the traditional Rossi-alpha method and the issue of requiring an excessive number of detectors with the DMD-Rossi-alpha method, this p...
Self-admitted technical debts (SATDs) refer to a solution in software development that selects suboptimal solutions to meet the current requirements and are intentionally introduced and documented by developers. SATDs in issue-tracking systems are a...
The coal mining industry has accumulated a vast amount of knowledge on shearer accident analysis and handling during its development. Accurately identifying and extracting entity information related to shearer maintenance is crucial for advancing dow...
In producing English-Chinese bilingual maps, it is usually necessary to translate English place names into Chinese. However, pipeline-based methods for translating place names splits the place name translation task into multiple sub-tasks, carries th...
Regarding the scarcity of annotated data for existing event extraction tasks and the insufficient semantic mining of event extraction models in the Chinese domain, this paper proposes a generative joint event extraction model to improve existing mode...
The drilling core sampling and chemical analysis method for the quantitative determination of solid mineral deposits has several drawbacks, including a low core drilling efficiency, a high core sampling cost, and a long chemical analysis cycle. In cu...
The intelligent maintenance of coal mining equipment is crucial for ensuring safe production in coal mines. Despite the rapid development of large language models (LLMs) injecting new momentum into the intelligent transformation and upgrading of coal...
In the field of natural language processing (NLP), prompt-based learning is widely used for efficient parameter learning. However, this method has the drawback of shortening the input length by the extent of the attached prompt, leading to the ineffi...
Text classification aims to assign predefined labels to unlabeled sentences, which tend to struggle in real-world applications when only a few annotated samples are available. Previous works generally focus on using the paradigm of meta-learning to o...
This study proposes an Aspect-Enhanced Prompting (AEP) method for unsupervised Multi-Source Domain Adaptation in Aspect Sentiment Classification, where data from the target domain are completely unavailable for model training. The proposed AEP is bas...
Recently, prompt-based methods have achieved a promising performance in many natural language processing benchmarks. Despite success in sentence-level classification tasks, prompt-based methods work poorly in token-level tasks, such as named entity r...
Dialogue relation extraction identifies semantic relations between entity pairs in dialogues. This research explores a methodology harnessing the potential of prompt-based fine-tuning paired with a trigger-generation approach. Capitalizing on the int...
Cyberspace search engines (CSEs) are systems designed to search and index information about cyberspace assets. Effectively mining data across diverse platforms is hindered by the complexity and diversity of different CSE syntaxes. While Text-to-CSEQL...
The proliferation of fake news poses a significant challenge in today’s information landscape, spanning diverse domains and topics and undermining traditional detection methods confined to specific domains. In response, there is a growing inter...
Recently, prompt-based learning has shown impressive performance on various natural language processing tasks in few-shot scenarios. The previous study of knowledge probing showed that the success of prompt learning contributes to the implicit knowle...
With the rapid development of multimodal prompt learning in unsupervised domains, prompt tuning has demonstrated significant potential for dense counting tasks. However, existing supervised methods heavily rely on annotated data, limiting their gener...
Background: Pre-Trained Language Models hold significant promise for revolutionizing mental health care by delivering accessible and culturally sensitive resources. Despite this potential, their efficacy in mental health applications, particularly in...
Topic modeling is a fundamental technique in natural language processing used to uncover latent themes in large text corpora, yet existing approaches struggle to jointly achieve interpretability, semantic coherence, and scalability. Classical probabi...
Political perspective detection in news media—identifying political bias in news articles—is an essential but challenging low-resource task. Prompt-based learning (i.e., discrete prompting and prompt tuning) achieves promising results in...
Selective de-identification is a key technology for protecting sensitive objects in visual data while preserving meaningful information. This study proposes a framework that leverages text prompt-based zeroshot and referring object detection techniqu...
Recent advances in prompt learning have opened new avenues for enhancing natural language understanding in domain-specific tasks, including code vulnerability detection. Motivated by the limitations of conventional binary classification methods in ca...
Existing studies on backdoor attacks in large language models (LLMs) have contributed significantly to the literature by exploring trigger-based strategies—such as rare tokens or syntactic anomalies—that, however, limit both their stealth...
Natural language understanding is a crucial aspect of task-oriented dialogue systems, encompassing intent detection (ID) and slot filling (SF). Conventional approaches for ID and SF solve the problems in a separate manners, while recent studies are n...
Named entity recognition (NER) in a few-shot setting is an extremely challenging task, and most existing methods fail to account for the gap between NER tasks and pre-trained language models. Although prompt learning has been successfully applied in...
In social networks, the occurrence of unexpected events rapidly catalyzes the widespread dissemination and further evolution of network public opinion. The advent of zero-shot stance detection aligns more closely with the characteristics of stance de...
Question answering over domain-specific knowledge graphs implies several challenges. It requires sufficient knowledge of the world and the domain to understand what is being asked, familiarity with the knowledge graph’s structure to build a cor...
At present, researchers are showing a marked interest in the topic of few-shot named entity recognition (NER). Previous studies have demonstrated that prompt-based learning methods can effectively improve the performance of few-shot NER models and ca...
This paper explores the emerging potential of large language models (LLMs) and generative AI for social network analysis (SNA) based on open-ended survey data as a source. We introduce a novel methodology, Survey-to-Multilayer Network (SURVEY2MLN), w...
Phishing URL detection is critical due to the severe cybersecurity threats posed by phishing attacks. While traditional methods rely heavily on handcrafted features and supervised machine learning, recent advances in large language models (LLMs) prov...
This paper explores the emerging security risks in Large Language Models (LLMs) through a comparative study of jailbreaking techniques. These adversarial methods exploit linguistic and alignment weaknesses in LLMs to bypass content safeguards and gen...
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...
Landscape Character Assessments (LCAs) support planning decisions by offering structured descriptions of landscape character. However, producing these texts is often resource-intensive and shaped by subjective judgement. This study explores whether G...
Medical image segmentation is a vital healthcare endeavor requiring precise and efficient models for appropriate diagnosis and treatment. Vision transformer (ViT)-based segmentation models have shown great performance in accomplishing this task. Howe...
Due to their extensive common-sense knowledge and linguistic understanding, large language models (LLMs) have demonstrated remarkable capabilities in text comprehension and logical reasoning for natural language processing tasks. Traditional prompt-b...
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...
Deep learning techniques have demonstrated significant advancements in the task of text classification. Regrettably, the majority of these techniques necessitate a substantial corpus of annotated data to achieve optimal performance. Meta-learning has...
The integration of Large Language Models (LLMs), particularly Visual Language Models (VLMs), into robotics promises more intuitive human–robot interactions; however, challenges remain in efficiently translating high-level commands into precise...
Text classification is a machine learning technique employed to assign a given text to predefined categories, facilitating the automatic analysis and processing of textual data. However, an important problem is that the number of new text categories...
Investigating public attitudes towards social media is crucial for opinion mining systems to gain valuable insights. Stance detection, which aims to discern the attitude expressed in an opinionated text towards a specific target, is a fundamental tas...
Background/Objectives: Precise breast ultrasound (BUS) segmentation supports reliable measurement, quantitative analysis, and downstream classification yet remains difficult for small or low-contrast lesions with fuzzy margins and speckle noise. Text...
Remote sensing image classification has achieved remarkable success in environmental monitoring and urban planning using deep neural networks (DNNs). However, the performance of these models is significantly impacted by domain shifts due to seasonal...
Objective: Named entity recognition (NER) offers a powerful method for automatically extracting key clinical information from text, but current models often lack sufficient support for non-English languages. Materials and Methods: This study investig...
Accurate segmentation of cellular structures in whole slide images (WSIs) is essential for quantitative analysis in computational pathology. However, the complexity and scale of WSIs present significant challenges for conventional segmentation method...
In the development of goal-oriented dialogue systems, neural network topic modeling and clustering methods are traditionally used to extract user intentions and operator response scenario blocks. The emergence of generative large language models allo...
Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models. However, these methods encounter significant challenges...
Data scarcity is a significant barrier to developing high-performing AI models for medical text classification. To improve stroke prediction from electrocardiogram (ECG) interpretations reports where training data are scarce, we propose a novel data...
Knowledge graphs (KGs) offer a structured and collaborative approach to integrating diverse knowledge from various domains. However, constructing knowledge graphs typically requires significant manual effort and heavily relies on pretrained models, l...
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