The advancement of Large Language Models (LLMs) has significantly transformed the field of natural language processing, although the focus on English-centric models has created a noticeable research gap for specific languages, including Vietnamese. To address this issue, this paper presents vi-mistral-x, an innovative Large Language Model designed expressly for the Vietnamese language. It utilizes a unique method of continual pre-training, based on the Mistral architecture, which incorporates grouped-query attention and sliding window attention techniques. This model, vi-Mistral-X, marks a significant step forward in improving the understanding and generation of the Vietnamese language. It introduces an additional phase of continual pre-training, specifically adapted for Vietnamese, enhancing the model's capability in understanding complex language nuances and generating accurate, context-aware Vietnamese text. Through comprehensive testing on various benchmarks, vi-mistral-x has shown to outperform existing Vietnamese LLMs in several key areas, including text classification, question answering, and text generation. Particularly, in the Vietnamese Multitask Language Understanding (VMLU) benchmark, vi-mistral-x sets a new standard, outperforming other available models significantly. This paper highlights the critical role of continual pre-training in advancing language-specific LLMs and opens new avenues for the development of multilingual models. We aim for vi-mistral-x to not just be an important asset for processing the Vietnamese language but also to encourage more advancements in creating large language models for languages that are less represented.

Image captioning is a crucial task with applications in a wide range of domains, including healthcare and education. Despite extensive research on English image captioning datasets, the availability of such datasets for Vietnamese remains limited, with only two existing datasets. In this study, we introduce KTVIC, a comprehensive Vietnamese Image Captioning dataset focused on the life domain, covering a wide range of daily activities. This dataset comprises 4,327 images and 21,635 Vietnamese captions, serving as a valuable resource for advancing image captioning in the Vietnamese language. We conduct experiments using various deep neural networks as the baselines on our dataset, evaluating them using the standard image captioning metrics, including BLEU, METEOR, CIDEr, and ROUGE. Our findings underscore the effectiveness of the proposed dataset and its potential contributions to the field of image captioning in the Vietnamese context.

The surge in Large Language Models (LLMs) such as ChatGPT and GPT-4 has significantly advanced the field of artificial intelligence (AI), particularly in language processing. In 2023, Vietnam's AI sector witnessed a notable development with the introduction of several Vietnamese-centric LLMs, including BLOOMZ's Vietcuna, URA-LLaMA, PhoGPT, and dama-2. Amidst this progression, we introduce VinaLLaMA, a foundational LLM designed specifically for the Vietnamese language. VinaL-LaMA, built on top of LLaMA-2, represents a vital stride towards linguistic inclusivity in AI, adeptly addressing the syntactic and semantic intricacies of Vietnamese. Embracing the spirit of collaboration and open innovation, we are pleased to announce VinaLLaMA, an open-weight Foundation Language Model and its chat variant. These models are now accessible on HuggingFace, ensuring compatibility with all'transformers' framework-supported libraries. This endeavor not only contributes to the global AI research landscape but also provides a specialized tool for exploring and enhancing Vietnamese language processing, encouraging a wider engagement and application in AI-driven NLP research.

Recent studies have provided empirical evidence of the wide-ranging potential of Generative Pre-trained Transformer (GPT), a pretrained language model, in the field of natural language processing. GPT has been effectively employed as a decoder within state-of-the-art (SOTA) question answering systems, yielding exceptional performance across various tasks. However, the current research landscape concerning GPT's application in Vietnamese remains limited. This paper aims to address this gap by presenting an implementation of GPT-2 for community-based question answering specifically focused on COVID-19 related queries in Vietnamese. We introduce a novel approach by conducting a comparative analysis of different Transformers vs SOTA models in the community-based COVID-19 question answering dataset. The experimental findings demonstrate that the GPT-2 models exhibit highly promising outcomes, outperforming other SOTA models as well as previous community-based COVID-19 question answering models developed for Vietnamese.

In recent years, Visual Question Answering (VQA) has gained significant attention for its diverse applications, including intelligent car assistance, aiding visually impaired individuals, and document image information retrieval using natural language queries. VQA requires effective integration of information from questions and images to generate accurate answers. Neural models for VQA have made remarkable progress on large-scale datasets, with a primary focus on resource-rich languages like English. To address this, we introduce the ViCLEVR dataset, a pioneering collection for evaluating various visual reasoning capabilities in Vietnamese while mitigating biases. The dataset comprises over 26,000 images and 30,000 question-answer pairs (QAs), each question annotated to specify the type of reasoning involved. Leveraging this dataset, we conduct a comprehensive analysis of contemporary visual reasoning systems, offering valuable insights into their strengths and limitations. Furthermore, we present PhoVIT, a comprehensive multimodal fusion that identifies objects in images based on questions. The architecture effectively employs transformers to enable simultaneous reasoning over textual and visual data, merging both modalities at an early model stage. The experimental findings demonstrate that our proposed model achieves state-of-the-art performance across four evaluation metrics. The accompanying code and dataset have been made publicly accessible at \url{https://github.com/kvt0012/ViCLEVR}. This provision seeks to stimulate advancements within the research community, fostering the development of more multimodal fusion algorithms, specifically tailored to address the nuances of low-resource languages, exemplified by Vietnamese.

Question answering (QA) is a natural language understanding task within the fields of information retrieval and information extraction that has attracted much attention from the computational linguistics and artificial intelligence research community in recent years because of the strong development of machine reading comprehension-based models. A reader-based QA system is a high-level search engine that can find correct answers to queries or questions in open-domain or domain-specific texts using machine reading comprehension (MRC) techniques. The majority of advancements in data resources and machine-learning approaches in the MRC and QA systems especially are developed significantly in two resource-rich languages such as English and Chinese. A low-resource language like Vietnamese has witnessed a scarcity of research on QA systems. This paper presents XLMRQA, the first Vietnamese QA system using a supervised transformer-based reader on the Wikipedia-based textual knowledge source (using the UIT-ViQuAD corpus), outperforming the two robust QA systems using deep neural network models: DrQA and BERTserini with 24.46% and 6.28%, respectively. From the results obtained on the three systems, we analyze the influence of question types on the performance of the QA systems.