In the domain of natural language processing (NLP), a primary challenge pertains to the process of Chinese tokenization, which remains challenging due to the lack of explicit word boundaries in written Chinese. The existing tokenization methods often...
Large language models (LLMs) have become central to how people access and interact with information, yet their potential to reproduce ableist bias remains underexamined, especially in non-English-language settings. This study examines disability repr...
In the product design and manufacturing process, the effective management and representation of system requirements (SRs) are crucial for ensuring product quality and consistency. However, current methods are hindered by document ambiguity, weak requ...
This paper focuses on the efficient fine-tuning of large language models within the federated learning framework. To address the performance bottlenecks caused by multi-source heterogeneity and structural inconsistency, a structure-aware federated fi...
In the last few decades, text mining has been used to extract knowledge from free texts. Applying neural networks and deep learning to natural language processing (NLP) tasks has led to many accomplishments for real-world language problems over the y...
Natural language models brought rapid developments to Natural Language Processing (NLP) performance following the emergence of large-scale deep learning models. Language models have previously used token units to represent natural language while redu...
Bridge structures are considered complex and significant. Accordingly, the knowledge of the engineering domain of bridge construction and related specialist areas is multidimensional and highly specific. Sometimes this knowledge is explicitly documen...
Learning sentence representations is an essential and challenging topic in the deep learning and natural language processing communities. Recent methods pre-train big models on a massive text corpus, focusing mainly on learning the representation of...
Natural-language-driven robot navigation in complex indoor environments requires the joint capability of high-fidelity scene representation, structured semantic reasoning, and executable path planning. To address this challenge, this paper proposes H...
The commonsense question and answering (CSQA) system predicts the right answer based on a comprehensive understanding of the question. Previous research has developed models that use QA pairs, the corresponding evidence, or the knowledge graph as an...
The extensive use of large language models (LLMs), particularly in the finance sector, raises concerns about the authenticity and reliability of generated text. Developing a robust method for distinguishing between human-written and AI-generated fina...
12 May 2022
The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent...
Driving safety hinges on the dynamic interplay between task demand and driving capability, yet these concepts lack a unified, quantifiable formulation. In this work, we present a framework based on a multimodal large language model that transforms he...
Deep learning has extensively been used in natural language processing with sub-word representation vectors playing a critical role. However, this cannot be said of Swahili, which is a low resource and widely spoken language in East and Central Afric...
In natural language processing, text needs to be transformed into a machine-readable representation before any processing. The quality of further natural language processing tasks greatly depends on the quality of those representations. In this surve...
Assessing semantic representations learned by language models remains challenging because conventional intrinsic benchmarks capture limited aspects of meaning and often correlate weakly with downstream performance. Eye tracking offers complementary,...
In today’s world, where information keeps growing rapidly and changing constantly, language models play a crucial role in making our lives easier across different fields. However, it is tough to keep these models updated with all the new data w...
Pre-trained transformer models shine in many natural language processing tasks and therefore are expected to bear the representation of the input sentence or text meaning. These sentence-level embeddings are also important in retrieval-augmented gene...
The subject of this paper is the problem of knowledge revealed by the emergence of advanced natural language processing technology, called large language models (LLMs). It proposes an interpretation of the appearance of this phenomenon as an emergent...
Natural-language access to spatial databases requires relation interpretation, entity grounding, metric normalization, and database-specific execution semantics. Direct generation of Structured Query Language (SQL) by large language models (LLMs) can...
As large language models (LLMs) scale to cover more languages, their potential to support low-resource settings becomes increasingly promising. However, the mechanisms underlying cross-lingual transfer and the factors that facilitate it remain insuff...
Autoencoder-based models have become a fundamental component of unsupervised and self-supervised learning in natural language processing (NLP), enabling models to learn compact latent representations through input reconstruction. From early denoising...
Large Language Models (LLMs) are typically safety-aligned using high-resource language data, but it remains unclear whether these constraints transfer reliably across distinct linguistic manifolds. This study examines the mathematical foundations of...
Large language models (LLMs) have demonstrated remarkable capabilities in learning complex tasks purely from sequential data. To explore whether such models can internalize strategic world representations, We investigate whether generative transforme...
As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or...
Large language models (LLMs) have become central to modern artificial intelligence, yet their internal decision-making processes remain difficult to interpret. As interest grows in making these models more transparent and reliable, topological data a...
In recent years, cross-modal retrieval has garnered significant attention due to its potential to bridge heterogeneous data modalities, particularly in aligning visual content with natural language. Despite notable progress, existing methods often st...
Open-vocabulary semantic segmentation (OVSS) is of critical importance for unmanned aerial vehicle (UAV) imagery, as UAV scenes are highly dynamic and characterized by diverse, unpredictable object categories. Current OVSS approaches mainly rely on t...
The detection of fake news has emerged as a crucial area of research due to its potential impact on society. In this study, we propose a robust methodology for identifying fake news by leveraging diverse aspects of language representation and incorpo...
Large language models (LLMs) can produce inaccurate answers even when relevant facts are supplied in the prompt. This study examines how graph data serialization, entity identifiers, and context selection affect factual question answering in knowledg...
In the rapidly evolving landscape of cancer drug research, cellular lines serve as invaluable tools for understanding drug-sensitive and drug-resistant tumors. The computational representation of cellular lines is usually based on genomic profiling,...
Geospatial data contains abundant temporal, spatial, and attribute information related to geographic entities, serving as a critical foundation for the development of new quality productive forces. Achieving effective semantic representation of geogr...
Deployable autonomy remains a key challenge for unmanned aerial vehicles (UAVs) operating in open-ended missions. Large language models (LLMs) and their multimodal variants, which can process visual and other sensory inputs, have introduced new capab...
In the past decade, personalized treatment has become increasingly significant; however, the individualized care provided by physicians incurs substantial labor costs. The advancement of artificial intelligence, particularly the emergence of large la...
Topic modeling is a crucial technique for Natural Language Processing (NLP) which helps to automatically uncover coherent topics from large-scale text corpora. Yet, classic methods tend to suffer from poor semantic depth and topic coherence. In this...
Artificial intelligence and big data are transforming chemistry: the field is shifting from a discipline in which observations are mainly interpreted after experiments into a domain in which data, models, and experimental research form a unified, int...
Text-attributed graphs (TAGs) require models to jointly exploit node text and graph structure, yet doing so effectively remains difficult when node text is sparse and the structural context is large. Here, we propose STAGE (Semantic and Topological A...
Large Language Models (LLMs) have transformed Natural Language Processing (NLP), yet they continue to struggle with deep semantic understanding, particularly in tasks like coreference resolution and structured semantic inference. This study presents...
This article presents a state-of-the-art review of recent advances aimed at transforming traditional Failure Mode and Effects Analysis (FMEA) into a more intelligent, data-driven, and semantically enriched process. As engineered systems grow in compl...
Recent advancements in graph-based text representation, particularly with embedding models and transformers such as BERT, have shown significant potential for enhancing natural language processing (NLP) tasks. However, challenges related to data spar...
Recent advancements in multimodal large language models (MLLMs) have demonstrated remarkable capabilities in processing diverse data types, yet significant disparities persist between human cognitive processes and computational approaches to multimod...
Large language models are increasingly used for building fault diagnosis, but input representation is often changed alongside model adaptation or added knowledge. This study isolates serialization effects using matched rooftop unit measurements rende...
Place descriptions record qualitative information related to places and their spatial relationships; thus, the geospatial semantics of a place can be extracted from place descriptions. In this study, geotagged microblog short texts recorded in 2017 f...
Background/Objectives: Health and social care systems around the globe are currently undergoing a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, condition...
The configuration of AUTOSAR Basic Software (BSW) modules relies on verbose AUTOSAR XML (ARXML) files that are complex, error-prone, and costly to produce manually—particularly for safety-critical modules governed by ISO 26262. This paper prese...
The generalization of safety mechanisms from text-only LLMs to Large Vision–Language Models (LVLMs) remains poorly understood, especially concerning their vulnerability to typographic adversarial prompts. In this paper we ask where, inside a mo...
Temporal knowledge graph forecasting aims to predict future missing facts from continuously evolving relational data in dynamic information systems. Such forecasting tasks are increasingly important for intelligent applications involving complex inte...
The purpose of this survey study is to shed light on the importance of knowledge usage and knowledge-driven applications in telecommunication systems and businesses. To this end, we first define a classification of the different knowledge-based appro...
We extended real estate property listings on the online prop-tech platform. On the platform, the images were classified into the specified classes according to quality criteria. The necessary interventions were made by measuring the platform’s...
Automated system architecture generation is a key task in Model-Based Systems Engineering (MBSE), where requirement and functional models need to be transformed into a physical architecture composed of selected components and port-level connections....
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