• The Korean Journal of Physiology and Pharmacology
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• v.28 no.5
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• pp.393-401
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• 2024

Large language models (LLMs) are rapidly transforming medical writing and publishing. This review article focuses on experimental evidence to provide a comprehensive overview of the current applications, challenges, and future implications of LLMs in various stages of academic research and publishing process. Global surveys reveal a high prevalence of LLM usage in scientific writing, with both potential benefits and challenges associated with its adoption. LLMs have been successfully applied in literature search, research design, writing assistance, quality assessment, citation generation, and data analysis. LLMs have also been used in peer review and publication processes, including manuscript screening, generating review comments, and identifying potential biases. To ensure the integrity and quality of scholarly work in the era of LLM-assisted research, responsible artificial intelligence (AI) use is crucial. Researchers should prioritize verifying the accuracy and reliability of AI-generated content, maintain transparency in the use of LLMs, and develop collaborative human-AI workflows. Reviewers should focus on higher-order reviewing skills and be aware of the potential use of LLMs in manuscripts. Editorial offices should develop clear policies and guidelines on AI use and foster open dialogue within the academic community. Future directions include addressing the limitations and biases of current LLMs, exploring innovative applications, and continuously updating policies and practices in response to technological advancements. Collaborative efforts among stakeholders are necessary to harness the transformative potential of LLMs while maintaining the integrity of medical writing and publishing.

• Korean Journal of Computational Design and Engineering
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• v.21 no.1
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• pp.78-89
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• 2016

When a plant owner requests plant 3D CAD models in the format that a shipbuilding company does not use, the shipyard manually re-models plant 3D CAD models according to the owner's requirement. Therefore, it is important to develop a technology to compare the re-modeled plant 3D CAD models with original ones and to quantitatively evaluate similarity between two models. In the previous study, we developed a graphic user interface (GUI)-based comparison system where a user evaluates similarity between original and re-modeled plant 3D CAD models for piping design at the level of unit. However, an offshore plant consists of thousands of units and thus a system which compares several plant 3D CAD models at unit-level without human intervention is necessary. For this, we developed a new batch model comparison system which automatically evaluates similarity of several unit-level plant 3D CAD models using an extensible markup language (XML) file storing file location and name data about a set of plant 3D CAD models. This paper suggests system configuration of a batch-mode-based comparison system and discusses its core functions. For the verification of the developed system, comparison experiments for offshore plant 3D piping CAD models using the system were performed. From the experiments, we confirmed that similarities for several plant 3D CAD models at unit-level were evaluated without human intervention.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.619-633
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• 1993

A new High level hardware Description Language, ASPHODEL(Algorithm Synthesis Pascal Hardware for Optimal Design and Efficient Language), and its algorithm compiler for high level synthesis are described in this paper. The new HDL, appropriated to the description of algorithmic level and lower, models VLSI circuits as an abstracted block which is consisted of input/output ports and hierachical processors to control VLSI complexities with efficiency. Also, in order to improve the descriptive power, popular Pascal programming language is modified to build ASPHODEL syntax rules. ASPHODEL algorithm compiler generates an intermediate form through lexical and syntax analysis from ASPHODEL source codes. To show the validation of presented language and its compiler, those are applied to practical design examples.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.241-246
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• 2004

In this paper, we propose VCCV units as a processing unit of language model and compare them with clauses and morphemes of existing processing units. Clauses and morphemes have many vocabulary and high perplexity. But VCCV units have low perplexity because of the small lexicon and the limited vocabulary. The construction of language models needs an issue of the smoothing. The smoothing technique used to better estimate probabilities when there is an insufficient data to estimate probabilities accurately. This paper made a language model of morphemes, clauses and VCCV units and calculated their perplexity. The perplexity of VCCV units is lower than morphemes and clauses units. We constructed the N-grams of VCCV units with low perplexity and tested the language model using Katz, absolute, modified Kneser-Ney smoothing and so on. In the experiment results, the modified Kneser-Ney smoothing is tested proper smoothing technique for VCCV units.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.273-283
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• 2022

Banking and finance is a broad term that incorporates a variety of smaller, more specialized subjects such as corporate finance, tax finance, and insurance finance. A virtual assistant that assists users in searching for information about banking and finance terms might be an extremely beneficial tool for users. In this study, we explored the process of searching for information, seeking opportunities, and developing a virtual assistant in the first stages of starting learning and understanding Vietnamese to increase effectiveness and save time, which is also an innovative business practice in Use-case Vietnam. We built the FIBA2020 dataset and proposed a pipeline that used Natural Language Processing (NLP) inclusive of Natural Language Understanding (NLU) algorithms to build chatbot applications. The open-source framework RASA is used to implement the system in our study. We aim to improve our model performance by replacing parts of RASA's default tokenizers with Vietnamese tokenizers and experimenting with various language models. The best accuracy we achieved is 86.48% and 70.04% in the ideal condition and worst condition, respectively. Finally, we put our findings into practice by creating an Android virtual assistant application using the model trained using Whitespace tokenizer and the pre-trained language m-BERT.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.31-39
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• 2023

The evolution of Natural Language Processing (NLP) and the rise of large language models (LLM) like ChatGPT have paved the way for specialized question-answering (QA) systems tailored to specific domains. This study outlines a system harnessing the power of LLM in conjunction with document search algorithms to interpret and address user inquiries using documents from the Korea Atomic Energy Research Institute (KAERI). Initially, the system refines multiple documents for optimized search and analysis, breaking the content into managable paragraphs suitable for the language model's processing. Each paragraph's content is converted into a vector via an embedding model and archived in a database. Upon receiving a user query, the system matches the extracted vectors from the question with the stored vectors, pinpointing the most pertinent content. The chosen paragraphs, combined with the user's query, are then processed by the language generation model to formulate a response. Tests encompassing a spectrum of questions verified the system's proficiency in discerning question intent, understanding diverse documents, and delivering rapid and precise answers.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.139-160
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• 2017

The purpose of this study is to find out the influence of reading role model (parents, teachers, and friends) and reading effectiveness (reading satisfaction, willingness to persistent reading, immersed reading) reflected on middle school students' academic achievement (Korean Language performance and middle school record). As a result of the analysis, the factors affecting the performance of Korean Language were father (1.9%) and mother (3.4%) among reading role models. Among reading effectiveness factors, willingness to persistent reading (8%) was the only effective factor. In the case of the middle school record, the mother (3.4%) had the only effect among the reading role models, and reading effectiveness was influenced all factors such willingness to persistent reading (10.4%), immersed reading (4.0%) and reading satisfaction (1.9%). However, except for the factor willingness to persistent reading, the it found the influence was insignificant.

• The Journal of the Acoustical Society of Korea
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• v.21 no.3
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• pp.274-284
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• 2002

Word segmentation errors occurring in text preprocessing often insert incorrect words into recognition vocabulary and cause poor language models for Korean large vocabulary continuous speech recognition. We propose an automatic word segmentation algorithm using Markov chains and syllable-based n-gram language models in order to correct word segmentation error in teat corpora. We assume that a sentence is generated from a Markov chain. Spaces and non-space characters are generated on self-transitions and other transitions of the Markov chain, respectively Then word segmentation of the sentence is obtained by finding the maximum likelihood path using syllable n-gram scores. In experimental results, the algorithm showed 91.58% word accuracy and 96.69% syllable accuracy for word segmentation of 254 sentence newspaper columns without any spaces. The algorithm improved the word accuracy from 91.00% to 96.27% for word segmentation correction at line breaks and yielded the decomposition accuracy of 96.22% for compound-noun decomposition.

• Journal of KIISE:Software and Applications
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• v.35 no.2
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• pp.128-136
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• 2008

Recognition of parallel structure at early stage of sentence parsing can reduce the complexity of parsing. In this paper, we propose an unsupervised language-independent probabilistic model for recongition of parallel noun structures. The proposed model is based on the idea of swapping constituents, which replies the properties of symmetry (two or more identical constituents are repeated) and of reversibility (the order of constituents is inter-changeable) in parallel structures. The non-symmetric patterns that cannot be captured by the general symmetry rule are resolved additionally by the modifier information. In particular this paper shows how the proposed model is applied to recognize Korean parallel noun phrases connected by "wa/kwa" particle. Our model is compared with other models including supervised models and performs better on recongition of parallel noun phrases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1413-1432
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• 2023

Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.