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This study pioneers a dual-stream network architecture tailored for Korean Natural Language Understanding (NLU), focusing on enhancing comprehension by distinct processing of syntactic and semantic aspects. The hypothesis is that this bifurcation can lead to a more nuanced and accurate understanding of the Korean language, which often presents unique syntactic and semantic challenges not fully addressed by generalized models. The validation of this novel architecture employs the Korean Natural Language Inference (koNLI) and Korean Semantic Textual Similarity (koSTS) datasets. By evaluating the model's performance on these datasets, the study aims to determine its efficacy in accurately parsing and interpreting Korean text's syntactic structure and semantic meaning. Preliminary results from this research are promising. They indicate that the dual-stream approach significantly enhances the model's capability to understand and interpret complex Korean sentences. This improvement is crucial in NLU, especially for language-specific applications. The implications of this study are far-reaching. The methodology and findings could pave the way for more sophisticated NLU applications tailored to the Korean language, such as advanced sentiment analysis, nuanced text summarization, and more effective conversational AI systems. Moreover, this research contributes significantly to the broader field of NLU by underscoring the importance and efficacy of developing language-specific models, moving beyond the one-size-fits-all approach of general language models. Thus, this study is a testament to the potential of specialized approaches in language understanding technologies. [ABSTRACT FROM AUTHOR]

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation, and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions, and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, and machine translation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG. [ABSTRACT FROM AUTHOR]

In this paper, we present a rigorous mathematical examination of generative pre-trained transformer (GPT) models and their autoregressive self-supervised learning mechanisms. We begin by defining natural language space and knowledge space, which are two key concepts for understanding the dimensionality reduction process in GPT-based large language models (LLMs). By exploring projection functions and their inverses, we establish a framework for analyzing the language generation capabilities of these models. We then investigate the GPT representation space, examining its implications for the models' approximation properties. Finally, we discuss the limitations and challenges of GPT models and their learning mechanisms, considering trade-offs between complexity and generalization, as well as the implications of incomplete inverse projection functions. Our findings demonstrate that GPT models possess the capability to encode knowledge into low-dimensional vectors through their autoregressive self-supervised learning mechanism. This comprehensive analysis provides a solid mathematical foundation for future advancements in GPT-based LLMs, promising advancements in natural language processing tasks such as language translation, text summarization, and question answering due to improved understanding and optimization of model training and performance. [ABSTRACT FROM AUTHOR]

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Natural language inference (NLI) is a hot research topic in natural language processing; contradiction-detection between sentences is a special case of NLI. This is considered to be a difficult NLP task that has a significant influence when added as a component in many NLP applications (such as questionanswering systems and text summarization). The Arabic language is one of the most challenging low-resource languages for detecting contradictions due to its rich lexical semantic ambiguity. We have created a data set of more than 12k sentences and named it ArNLI; it will be publicly available. Moreover, we have applied a new model that was inspired by Stanford's proposed contradictiondetection solutions for the English language. We proposed an approach for detecting contradictions between pairs of sentences in the Arabic language using a contradiction vector combined with a language model vector as an input to a machine-learning model. We analyzed the results of different traditional machine-learning classifiers and compared their results on our created data set (ArNLI) and on the automatic translation of both the PHEME and SICK English data sets. The best results were achieved by using the random forest classifier, with accuracies of 0.99, 0.60 and 0.75 on PHEME, SICK, and ArNLI respectively. [ABSTRACT FROM AUTHOR]

Large text documents are sometimes challenging to understand and time-consuming to extract vital information from. These issues are addressed by automatic text summarizing techniques, which condense lengthy texts while preserving their key information. Thus, the development of automatic summarization systems capable of fulfilling the ever-increasing demands of textual data becomes of utmost importance. It is even more vital with complex natural languages. This study explores five State-Of-The-Art (SOTA) Arabic deep Transformer-based Language Models (TLMs) in the task of text summarization by adapting various text summarization datasets dedicated to Arabic. A comparison against deep learning and machine learning-based baseline models has also been conducted. Experimental results reveal the superiority of TLMs, specifically the PEAGASUS family, against the baseline approaches, with an average F1-score of 90% on several benchmark datasets. [ABSTRACT FROM AUTHOR]