• Korean Journal of Radiology
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• v.24 no.10
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• pp.952-959
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• 2023

Large language models (LLMs) such as ChatGPT have garnered considerable interest for their potential to aid non-native English-speaking researchers. These models can function as personal, round-the-clock English tutors, akin to how Prometheus in Greek mythology bestowed fire upon humans for their advancement. LLMs can be particularly helpful for non-native researchers in writing the Introduction and Discussion sections of manuscripts, where they often encounter challenges. However, using LLMs to generate text for research manuscripts entails concerns such as hallucination, plagiarism, and privacy issues; to mitigate these risks, authors should verify the accuracy of generated content, employ text similarity detectors, and avoid inputting sensitive information into their prompts. Consequently, it may be more prudent to utilize LLMs for editing and refining text rather than generating large portions of text. Journal policies concerning the use of LLMs vary, but transparency in disclosing artificial intelligence tool usage is emphasized. This paper aims to summarize how LLMs can lower the barrier to academic writing in English, enabling researchers to concentrate on domain-specific research, provided they are used responsibly and cautiously.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.61-68
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• 2024

In this paper, Generative AI is a technology that creates various forms of content such as text, images, and music, and is being utilized across different fields. In the security sector, generative AI is poised to open up new possibilities in various areas including security vulnerability analysis, malware detection and analysis, and the creation and improvement of security policies. This paper presents a guide for identifying vulnerabilities and secure coding using ChatGPT for security vulnerability analysis and prediction, considering the application of generative AI in the security domain. While generative AI offers innovative possibilities in the security field, it is essential to continuously pursue research and development to ensure safe and effective utilization of generative AI through in-depth consideration of ethical and legal issues accompanying technological advancements.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.407-414
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• 2024

Social conflict surrounding public infrastructure projects has grown because of increasing project complexity and accelerated conflict propagation. This social conflict stems from various concerns ranging from environmental issues to regulatory and compliance requirements resulting in the intervention of various stakeholders with different interests. Against this backdrop, understanding the stakeholders involved and their dynamics is crucial for effective project management and smooth implementation of the project. Therefore, this paper introduces an analytical process utilizing ChatGPT to automatically identify stakeholders involved in the social conflict surrounding the public infrastructure project from news articles. As a result, a stakeholder network is constructed to delve into the complex stakeholder interrelationships and identify key stakeholders of the specific period. To explore the potential of the proposed process, an experimental case study of the Jeju 2^nd Airport project, which suffered from a high level of social conflict, was conducted. The proposed process enables timely analysis of the conflict situation which is crucial for successful conflict management. This study highlights the significance of a systemic approach to timely stakeholder analysis, setting the groundwork for a quantitative and up-to-date investigation of social conflicts around public infrastructure projects.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.432-436
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• 2023

초거대언어모델(LLM)의 도래에 따라 다양한 과업들이 도메인 관계 없이 제로샷으로 추론이 가능해짐에 따라서 LLM이 다양한 산업분야에 적용되고 있다. 대표적으로 ChatGPT와 GPT-4는 상용 API로 서비스를 제공하여 용이한 서비스 접근으로 다양한 이용층을 끌어들이고 있다. 그러나 현재 상용 API로 제공되고 있는 ChatGPT 및 GPT-4는 사용자의 대화 내역 데이터를 수집해 기업의 보안 문제를 야기할 수 있고 또한 생성된 결과물의 환각 문제로 인한 기업 문서의 신뢰성 저하를 초래할 수 있다. 특히 LLM 생성 글은 인간의 글과 유사한 수준으로 유창성을 확보한만큼 산업현장에서 LLM 작성 글이 판별되지 못할 경우 기업 활동에 큰 제약을 줄 수 있다. 그러나 현재 한국어 LLM 작성 글 탐지 서비스가 전무한 실정이다. 본 논문에서는 한국어 초거대언어모델 작성 글 판별기: KoCheckGPT 를 제안한다.KoCheckGPT는 산업현장에서 자주 사용되는 문어체, 개조식 글쓰기로 작성된 문서 도메인을 목표로 하여 글 전체와 문장 단위의 판별 정보를 결합하여 주어진 문서의 LLM 작성 여부를 효과적으로 판별한다. 다국어 LLM 작성 글 판별기 ZeroGPT와의 비교 실험 결과 KoCheckGPT는 우수한 한국어 LLM 작성 글 탐지 성능을 보였다.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.493-498
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• 2024

With the rapid advancement of generative artificial intelligence technology, there is growing interest in how to utilize it in practical applications. Additionally, the importance of prompt engineering to generate results that meet user demands is being newly highlighted. Exploring the new possibilities of generative AI can hold significant value. This study aims to utilize ChatGPT 4.0, a leading generative AI, to propose an effective method for evaluating user experience through the analysis of online customer review data. The user experience evaluation method was based on the six-layer elements of user experience: 'functionality', 'reliability', 'usability', 'convenience', 'emotion', and 'significance'. For this study, a literature review was conducted to enhance the understanding of prompt engineering and to grasp the clear concept of the user experience hierarchy. Based on this, prompts were crafted, and experiments for the user experience evaluation method were carried out using the analysis of collected online customer review data. In this study, we reveal that when provided with accurate definitions and descriptions of the classification processes for user experience factors, ChatGPT demonstrated excellent performance in evaluating user experience. However, it was also found that due to time constraints, there were limitations in analyzing large volumes of data. By introducing and proposing a method to utilize ChatGPT 4.0 for user experience evaluation, we expect to contribute to the advancement of the UX field.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.43-50
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• 2024

This paper evaluates the Korean sentiment analysis performance of large language models like GPT-3.5 and GPT-4 using a zero-shot approach facilitated by the ChatGPT API, comparing them to pre-trained Korean models such as KoBERT. Through experiments utilizing various Korean sentiment analysis datasets in fields like movies, gaming, and shopping, the efficiency of these models is validated. The results reveal that the LMKor-ELECTRA model displayed the highest performance based on F1-score, while GPT-4 particularly achieved high accuracy and F1-scores in movie and shopping datasets. This indicates that large language models can perform effectively in Korean sentiment analysis without prior training on specific datasets, suggesting their potential in zero-shot learning. However, relatively lower performance in some datasets highlights the limitations of the zero-shot based methodology. This study explores the feasibility of using large language models for Korean sentiment analysis, providing significant implications for future research in this area.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.12-20
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• 2024

Sentiment analysis has become a pivotal component in understanding public opinion, market trends, and user experiences across various domains. The advent of GPT (Generative Pre-trained Transformer) models has revolutionized the landscape of natural language processing, introducing a new dimension to sentiment analysis. This comprehensive roadmap delves into the transformative impact of GPT models on sentiment analysis tasks, contrasting them with conventional methodologies. With an increasing need for nuanced and context-aware sentiment analysis, this study explores how GPT models, known for their ability to understand and generate human-like text, outperform traditional methods in capturing subtleties of sentiment expression. We scrutinize various case studies and benchmarks, highlighting GPT models' prowess in handling context, sarcasm, and idiomatic expressions. This roadmap not only underscores the superior performance of GPT models but also discusses challenges and future directions in this dynamic field, offering valuable insights for researchers, practitioners, and AI enthusiasts. The in-depth analysis provided in this paper serves as a testament to the transformational potential of GPT models in the realm of sentiment analysis.

• Imaging Science in Dentistry
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• v.53 no.3
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• pp.267-267
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• 2023

• Construction Engineering and Management
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• v.24 no.4
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• pp.29-33
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• 2023

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.49 no.4
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• pp.239-240
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• 2023