With the increased complexity of applications and systems, threat modelling struggles to keep pace with the evolution of risks. This article addresses this challenge by exploring how large language models (LLMs) can be leveraged to create comprehensi...
The growing ubiquity of digital platforms has enabled unprecedented participation in large-scale group decision-making processes. Nevertheless, integrating subjective linguistically expressed opinions into structured decision protocols remains a sign...
AI language models are increasingly transforming language research in various ways. How can language educators and researchers respond to the challenge posed by these AI models? Specifically, how can we embrace this technology to inform and enhance s...
This paper provides an epistemological and methodological analysis of the recent practice of using neural language models to simulate brain language processing. It is argued that, on the one hand, this practice can be understood as an instance of the...
Since the emergence of ChatGPT, research on large language models (LLMs) has actively progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited exceptional abilities in understanding natural language and planning tasks...
Automatic speech recognition systems with a large vocabulary and other natural language processing applications cannot operate without a language model. Most studies on pre-trained language models have focused on more popular languages such as Englis...
Low-resource languages remain underserved by contemporary large language models (LLMs) because they lack sizable corpora, bespoke preprocessing tools, and the computing budgets assumed by mainstream alignment pipelines. Focusing on Kazakh, we present...
People have long desired intelligent conversational systems that can provide assistance in practical scenarios. The latest advancements in large language models (LLMs) are making significant strides toward turning this aspiration into a tangible real...
Parallel natural language processing systems were previously successfully tested on the tasks of part-of-speech tagging and authorship attribution through mini-language modeling, for which they achieved significantly better results than independent m...
The source of characters is hieroglyph. Hieroglyph is the imitation and reference of the phenomena and objects in the natural world. The objects and phenomena in the natural world are the original image. Or rather its own figurative meta-hieroglyphic...
The advent of Multilingual Language Models (MLLMs) and Large Language Models (LLMs) has spawned innovation in many areas of natural language processing. Despite the exciting potential of this technology, its impact on developing high-quality Machine...
Natural language processing (NLP) has significantly transformed in the last decade, especially in the field of language modeling. Large language models (LLMs) have achieved SOTA performances on natural language understanding (NLU) and natural languag...
This study focuses on Scene Text Recognition (STR), which plays a crucial role in various applications of artificial intelligence such as image retrieval, office automation, and intelligent transportation systems. Currently, pre-trained vision-langua...
Neural language models have drawn a lot of attention for their strong ability to predict natural language text. In this paper, we estimate the entropy rate of natural language with state-of-the-art neural language models. To obtain the estimate, we c...
Discourses can be treated as instances of knowledge. The dynamic space in which the trajectories of these discourses are described can be regarded as a model of knowledge. Such a space is called a discursive space. Its scope is defined by a set of di...
Sentiment analysis is a crucial tool for measuring public opinion and understanding human communication across digital social media platforms. However, due to linguistic complexities and limited data or computational resources, it is under-represente...
Large language and multimodal models are revolutionising many aspects of human work and creativity, with broad potential not only as chatbots and for information retrieval but as interaction points and integrators of large technical systems. However,...
Large language models (LLMs) have begun to function as assistants or teammates in language learning, teaching, and research. However, what prerequisites are required for LLMs to reliably play these roles, and how such prerequisites should be measured...
Pretrained language models have achieved great success in various natural language understanding (NLU) tasks due to their capacity to capture deep contextualized information in text using pretraining on large-scale corpora. Tokenization plays a signi...
This article provides a comprehensive survey of contemporary language modeling approaches within the realm of natural language processing (NLP) tasks. This paper conducts an analytical exploration of diverse methodologies employed in the creation of...
The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and, in particular, a shift toward Model-Based Systems Engineering (MBSE) approaches for system design. The requiremen...
Word segmentation is an essential task in automatic language processing for languages where there are no explicit word boundary markers, or where space-delimited orthographic words are too coarse-grained. In this paper we introduce the MiNgMatch Segm...
The rise in abusive language on social media is a significant threat to mental health and social cohesion. For Bengali speakers, the need for effective detection is critical. However, current methods fall short in addressing the massive volume of con...
The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These models exhibit the remarkable ability to provide proficient responses to free-text queries, demonstrating a nuanced unders...
Predicting student dropout from universities is an imperative but challenging task. Numerous data-driven approaches that utilize both student demographic information (e.g., gender, nationality, and high school graduation year) and academic informatio...
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...
Multi-task learning (MTL) approaches are actively used for various natural language processing (NLP) tasks. The Multi-Task Deep Neural Network (MT-DNN) has contributed significantly to improving the performance of natural language understanding (NLU)...
An important recent development in the field of linguistics is the use of small language models to investigate language acquisition. Following this line of research, we investigate the mysterious appearance of ‘negative concord’ (e.g., I ...
Building Information Modeling (BIM) has excellent potential to enhance building operation and maintenance. However, as a standardized data format in the architecture, engineering, and construction (AEC) industry, the retrieval of BIM information gene...
Because the pretraining model is not limited by the scale of data annotation and can learn general semantic information, it performs well in tasks related to natural language processing and computer vision. In recent years, more and more attention ha...
The usage of Artificial Intelligence (AI) in control loops and rule-based frameworks is a novel approach in automation and decision-making processes. Large Language Models (LLMs) are redefining conventional rule-based systems by introducing intuitive...
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...
In the field of edge-cloud computing environments, there is a continuous quest for new and simplified methods to automate the deployment and runtime adaptation to application lifecycle changes. Towards that end, cloud providers promote their own serv...
We study the performance of large language models (LLMs) in natural language understanding and natural language reasoning tasks in a low-resourced-language (LRL) setting. Using Yorùbá, an LRL, we curated a set of numerical probes with m...
Natural Language Processing (NLP) stands as a forefront of artificial intelligence research, empowering computational systems to comprehend and process human language as used in everyday contexts. Language models (LMs) underpin this field, striving t...
1 August 2009
Web services composition is an emerging paradigm for enabling inter and intra organizational integration, and a landscape of languages and techniques for modeling business processes in web service based environments has emerged and is continuously be...
Quantized large language models are large language models (LLMs) optimized for model size while preserving their efficacy. They can be executed on consumer-grade computers without the powerful features of dedicated servers needed to execute regular (...
The advent of online platforms and services has revolutionized communication, enabling users to share opinions and ideas seamlessly. However, this convenience has also brought about a surge in offensive and harmful language across various communicati...
Modeling languages have gained ever-increasing importance for the Internet of Things (IoT) domain for improving the productivity and quality of IoT developments. In this study, we analyzed 32 different modeling languages that have been designed for I...
Stance detection for low-resource languages, such as the Kuwaiti dialect, poses a significant challenge in natural language processing (NLP) due to the scarcity of annotated datasets and specialized tools. This study addresses these limitations by ev...
25 July 2023
Artificial intelligence (AI) and language models such as ChatGPT-4 (Generative Pretrained Transformer) have made tremendous advances recently and are rapidly transforming the landscape of medicine. Cardiology is among many of the specialties that uti...
The development of modern production systems involves numerous interdependent disciplines, heterogeneous data sources, and frequent design iterations, making the conceptual design phase particularly complex and error-prone. Model-Based Systems Engine...
Large language models have found utility in the domain of robot task planning and task decomposition. Nevertheless, the direct application of these models for instructing robots in task execution is not without its challenges. Limitations arise in ha...
In the field of software engineering, applying language models to the token sequence of source code is the state-of-the-art approach to building a code recommendation system. When applying language models to source code, it is difficult for state-of-...
In deep learning, large language models are typically trained on data from a corpus as representative of current knowledge. However, natural language is not an ideal form for the reliable communication of concepts. Instead, formal logical statements...
Recently, there have been significant advances and wide-scale use of generative AI in natural language generation. Models such as OpenAI’s GPT3 and Meta’s LLaMA are widely used in chatbots, to summarize documents, and to generate creative...
The decision-making process to rule R&D relies on information related to current trends in particular research areas. In this work, we investigated how one can use large language models (LLMs) to transfer the dataset and its annotation from one l...
With the continuous advancement of deep learning technology, pretrained language models have emerged as crucial tools for natural language processing tasks. However, optimization of pretrained language models is essential for specific tasks such as m...
Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Altho...
Recent advancements in planning prompting techniques for Large Language Models have improved their reasoning, planning, and action abilities. This paper develops a planning framework for Large Language Models using model predictive control that enabl...
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