ТЕХНІКИ ПРОМПТИНГУ ДЛЯ ПОКРАЩЕННЯ ВИКОРИСТАННЯ ВЕЛИКИХ МОВНИХ МОДЕЛЕЙ

Phishing attacks continue to grow in scale and sophistication, causing substantial financial losses and privacy breaches worldwide. Recent advances in large language models (LLMs) have brought significant changes to the generation and detection of phishing content. This study systematically investigates the dual role of LLMs in facilitating phishing attacks and strengthening countermeasures. Using the PRISMA methodology, authors screened 142 records published between January 2023 and April 2025 and identified 36 eligible studies from major academic databases, including IEEE Xplore, ScienceDirect, ACM Digital Library, Web of Science, and Scopus. A comprehensive and rigorous analysis was conducted of research trends/themes over time, dataset characteristics, and the LLM architectures/models employed. The findings reveal that most studies relied on manually generated datasets rather than publicly available benchmark datasets, and that GPT-based models received considerably more attention than other LLM architectures. The review demonstrates that LLMs substantially enhance the generation of phishing content by producing coherent, contextually relevant, and persuasive email and website content. This capability lowers the technical barrier for attackers and potentially increases attack effectiveness. Conversely, LLMs also strengthen defensive strategies by enabling more effective analysis of textual and visual content for phishing detection. In many cases, LLM-based approaches outperform traditional machine learning and deep learning methods and, in certain contexts, approach or match human-level performance. Overall, the findings suggest that LLMs have accelerated and automated phishing-related processes, simultaneously intensifying the threat landscape and advancing defensive capabilities. • The first in-depth study to review LLMs usage in Phishing attack generation and detection. • The study reveals LLMs have accelerated and automated phishing-related processes, elevating both threats and defence mechanisms. • GenAI-based multimodal phishing attacks are on the rise due to the wider adoption of GenAI tools in general.

Currently, AI is being used in various areas of human activity. Various large language models are becoming available to a wide range of users. For the average user, interacting with a large language model via a chatbot is the easiest, and for most users it is the only available option for interacting with a large language model.

In today’s rapidly evolving business landscape, Artificial Intelligence (AI), and specifically Large Language Models (LLMs), are redefining how organizations operate, make decisions, and engage with customers. AI-driven technologies have become indispensable, providing businesses with powerful tools to streamline operations, derive actionable insights from vast data, and foster more meaningful customer interactions. For business leaders, scholars, and practitioners alike, understanding the transformative potential of AI isn’t just advantageous—it’s essential to staying competitive in an increasingly data-driven world.This editorial delves into recent scholarly advancements in LLM applications within business contexts, analyzing studies that explore AI’s potential across various domains, from decision support to creative industries. By introducing a structured framework, this editorial highlights key insights and contributions from recent studies, assessing their value to academia and industry. The following comparative analysis sheds light on how these innovations shape our understanding of AI’s role in business while pointing to future research directions.Puyt and Madsen's (2024) study stands out as a foundational exploration of LLM accuracy, assessing ChatGPT-4's ability to recount the history of the SWOT analysis-a vital business strategy tool. Their findings reveal that, while ChatGPT-4 effectively conveys general concepts, it struggles with detailed historical information, often producing inaccuracies or "hallucinations." This gap underscores the need for LLMs to be trained with verified academic data, particularly for strategic business applications that demand precision. This study not only contributes to the literature by proposing methods to evaluate AI accuracy in historical contexts but also highlights the importance of rigorous information vetting in industry settings where reliability is crucial.In contrast, Raikov et al. (2024) explore a hybrid intelligence model that combines LLM capabilities with explainable AI (XAI) principles to enhance human-machine collaboration. Their approach emphasizes cognitive semantics, improving transparency and decision-making efficiency. The hybrid model's real-time adaptability addresses the needs of complex, regulated industries such as finance and healthcare, where trust in AI decisions is paramount. Academically, this study provides a valuable addition to XAI literature by demonstrating how LLMs can bridge the gap between AI autonomy and human oversight, making it a model for future human-AI interactions in complex business environments.Another significant study by Mariotti and colleagues (2024) examines the integration of LLMs with enterprise knowledge graphs to enhance data-driven decision-making. By enabling organizations to leverage knowledge graphs for more accurate and scalable data retrieval, this research provides a robust framework for businesses seeking efficient knowledge management systems. The academic contribution here lies in advancing the dialogue between LLMs and knowledge graphs, emphasizing ethical data handling and quality standards essential for industry applications. For enterprises, the study offers practical solutions to achieve streamlined data management, balancing automation with privacy and security. 2024) take a different approach, investigating LLMs' role in creative industries, specifically within fashion design. They introduce a hybrid intelligence model that supports creative processes, allowing AI to complement rather than replace human ingenuity. While LLMs in this field demonstrate potential in automating repetitive design tasks and enhancing customer personalization, the study reveals limitations in AI's ability to handle spatial and stylistic nuances. This study's academic contribution lies in promoting human-AI co-creation, inspiring further research into AI applications across diverse creative sectors, including media and marketing.Collectively, these studies not only illuminate LLMs' transformative potential in business but also highlight critical ethical and operational considerations. Ensuring accuracy, transparency, and data privacy are vital to responsibly integrating AI into business workflows. Future research should focus on enhancing LLM accuracy, refining hybrid intelligence models, and exploring creative AI applications, all while maintaining ethical standards. As LLMs evolve, interdisciplinary collaborations will be essential to harness their full potential, making AI an ethical, effective, and innovative force in the business world.

The advancement of large language models (LLMs) has yielded significant advancements across various domains. Nevertheless, this progress has also raised crucial concerns regarding privacy and security. The paper does a comprehensive literature study to thoroughly examine the fundamental principles of LLM. It also provides a detailed examination of the characteristics and application fields of various LLMs, with a particular focus on Transformer. Furthermore, this study places emphasis on the examination of privacy concerns that may emerge in the context of LLM's handling of personal and sensitive data. It also explores the potential hazards associated with information leakage and misuse, as well as the existing privacy safeguards and the obstacles encountered in their implementation. Overall, LLM has made significant advancements in technology. However, it is imperative to acknowledge the importance of doing research on safeguarding privacy and enhancing security. These aspects are vital for guaranteeing the sustained development and public confidence in LLM technology.

UNSTRUCTURED Misleading information has significant implications for society but can have disastrous impact for health matters. Transformative artificial intelligence (AI) tools such as large language models (LLMs) have the potential for limitless content generation (including fake), soon making internet information impossible to assess using traditional human approaches. We asked if the same LLMs (GPT4 and Gemini1-5-Pro) could be part of a more scalable solution. We tested 2 publicly available LLMs for their ability to identify misinformation in HealthReleases previously labeled by human experts. We found that simple prompts lead to overall low accuracy (F1 Macro 0,45 (GPT4) and 0,49 (Gemini1-5Pro)), but very different profiles for each LLM. Adding specific criteria used by experts to critically assess the Releases enhanced Gemini (0.66) but surprisingly reduced GPT4 (0,37) performances. We therefore developed a novel approach incorporating summaries of expert feedback into prompts and then observed major improvements in performance for both LLMs(GPT4;0.63 and Gemini1-5Pro; 0.96). Our study provides the first use case of LLMs as high throughput proofing of medical text, but more importantly provides insights into LLMs’ “truth biases”. We provide a novel paradigm integrating knowledge into the prompts which may reduce the need for LLM training, and the requirement for ever larger datasets and compute power. Importantly, we show how experts could and need to be involved in LLMs used to enhance their performance and potentially minimize the data wall issue.

With the continuous advancement of medical technologies, perioperative anesthesia management decisions are confronted with challenges of complexity and dynamicity, rendering traditional machine learning models insufficient for clinical needs. As one of the breakthrough applications in artificial intelligence, large language models (LLMs) may offer better help for intelligent anesthesia management. LLMs are capable of processing multidimensional and multi-source data, enabling more comprehensive prediction and intervention suggestions, thereby optimizing anesthesia management processes. This review summarizes the current applications of LLMs in anesthesiology and proposes methods for building specialized LLMs to tackle challenges in application. This work albeit mainly in the proposed stage aims to provide references for future development in this field and to promote in-depth research of LLMs in anesthesiology. Graphical Abstract

To assess the performance of state-of-the-art large language models in classifying vertebral metastasis stability using the Spinal Instability Neoplastic Score (SINS) compared to human experts, and to evaluate the impact of task-specific refinement including in-context learning on their performance. This retrospective study analyzed 100 synthetic CT and MRI reports encompassing a broad range of SINS scores. Four human experts (two radiologists and two neurosurgeons) and four large language models (Mistral, Claude, GPT-4 turbo, and GPT-4o) evaluated the reports. Large language models were tested in both generic form and with task-specific refinement. Performance was assessed based on correct SINS category assignment and attributed SINS points. Human experts demonstrated high median performance in SINS classification (98.5% correct) and points calculation (92% correct), with a median point offset of 0 [0-0]. Generic large language models performed poorly with 26-63% correct category and 4-15% correct SINS points allocation. In-context learning significantly improved chatbot performance to near-human levels (96-98/100 correct for classification, 86-95/100 for scoring, no significant difference to human experts). Refined large language models performed 71-85% better in SINS points allocation. In-context learning enables state-of-the-art large language models to perform at near-human expert levels in SINS classification, offering potential for automating vertebral metastasis stability assessment. The poor performance of generic large language models highlights the importance of task-specific refinement in medical applications of artificial intelligence.

Advancements in large language model accuracy for answering physical medicine and rehabilitation board review questions.

Recently, large language models (LLMs) have attracted considerable attention due to their remarkable capabilities. However, LLMs’ generation of biased or hallucinatory content raised significant concerns, posing major challenges for their practical application. Many studies have dedicated efforts to address these critical issues, adopting various approaches to mitigate bias and hallucinations in LLM-generated content. Remarkably, no review papers have synthesized insights on these two primary problems. Addressing this gap, this paper aims to conduct a simultaneous and dual-focused review of the current landscape of research. The discussions encompass widely used and newly proposed benchmarks and evaluation methods on bias and hallucination in LLMs. This paper also investigates advanced mitigation methods and present a taxonomy based on different mitigation strategies. Moreover, a comparative analysis of the sources, mitigation methods, and evaluation methods for bias and hallucination is included. In the end, this paper provides a synthesis of current research trends and suggests potential directions for future research to address bias and hallucination in LLMs, considering the ongoing challenges in this field.

The emergence of large language models (LLMs) and the increasing prevalence of electronic health records (EHRs) present significant opportunities for advancing health care research and practice. However, research that compares and applies LLMs to extract key epilepsy-related information from unstructured medical free text is under-explored. This study fills this gap by comparing and applying different open-source LLMs and methods to extract epilepsy information from unstructured clinic letters, thereby optimizing EHRs as a resource for the benefit of epilepsy research. We also highlight some limitations of LLMs. Employing a dataset of 280 annotated clinic letters from King's College Hospital, we explored the efficacy of open-source LLMs (Llama and Mistral series) for extracting key epilepsy-related information, including epilepsy type, seizure type, current anti-seizure medications (ASMs), and associated symptoms. The study used various extraction methods, including direct extraction, summarized extraction, and contextualized extraction, complemented by role-prompting and few-shot prompting techniques. Performance was evaluated against a gold standard dataset, and was also compared to advanced fine-tuned models and human annotations. Llama 2 13b (a 13-billion-parameter LLM developed by Meta) demonstrated superior extraction capabilities across tasks by consistently outperforming other LLMs (F1 = .80 in epilepsy-type extraction, F1 = .76 in seizure-type extraction, and F1 = .90 in current ASMs extraction). Here, F1 score is a balanced metric indicating the model's accuracy in correctly identifying relevant information without excessive false positives. The study highlights the direct extraction showing consistent high performance. Comparative analysis showed that LLMs outperformed current approaches like MedCAT (Medical Concept Annotation Tool) in extracting epilepsy-related information (.2 higher in F1). The results affirm the potential of LLMs in medical information extraction relating to epilepsy, offering insights into leveraging these models for detailed and accurate data extraction from unstructured texts. The study underscores the importance of method selection in optimizing extraction performance and suggests a promising avenue for enhancing medical research and patient care through advanced natural language processing technologies.

Large Language Models (LLMs) have become integral to a wide spectrum of applications, ranging from traditional computing tasks to advanced artificial intelligence (AI) applications. This widespread adoption has spurred extensive research into LLMs across various disciplines, including the social sciences. Notably, studies have revealed that LLMs possess emotional intelligence, which can be further developed through positive emotional stimuli. This discovery raises an intriguing question: can negative emotions similarly influence LLMs, potentially enhancing their performance? In response to this question, we introduce NegativePrompt, a novel approach underpinned by psychological principles, involving ten specifically designed negative emotional stimuli. We embark on rigorous experimental evaluations of five LLMs including Flan-T5-Large, Vicuna, Llama 2, ChatGPT, and GPT-4, across a set of 45 tasks. The results are revealing: NegativePrompt markedly enhances the performance of LLMs, evidenced by relative improvements of 12.89% in Instruction Induction tasks and 46.25% in BIG-Bench tasks. Moreover, we conduct attention visualization experiments to decipher the underlying mechanisms of NegativePrompt's influence. Our research contributes significantly to the understanding of LLMs and emotion interaction, demonstrating the practical efficacy of NegativePrompt as an emotion-driven method and offering novel insights for the enhancement of LLMs in real-world applications. The code is available at https://github.com/wangxu0820/NegativePrompt.

As technology continues to advance, it becomes increasingly integrated into daily life facilitating complex tasks across a range of environments. While some applications such as smartphones and smartwatches are less critical, others like healthcare devices and autonomous vehicles demand bug-free performance to prevent financial loss or harm. Traditionally, simulation-based testing and formal verification played a major role in ensuring a bug-free device. However, the simulation of bigger systems is limited to a definite number of scenarios on the Design under Verification (DUV). Hence, it is unable to explore all possible inputs that can occur. Formal verification, on the other hand, offers a higher level of assurance through mathematical proofs but is both time-consuming and suffers from scalability issues, especially as designs grow in complexity. Recently, Large Language Models (LLMs) have shown promise in tasks previously limited to human expertise. Their natural language processing capabilities can assist in handling extensive specifications and source code, particularly in debugging hardware descriptions and analyzing security and functionality. The utilization of Retrieval Augmented Generation (RAG) has further enhanced LLMs by incorporating large specification or source code bases, thereby improving their bug-identification and correction capabilities. While recent advancements in LLMs, particularly with RAG, have yielded promising results in bug identification and correction for a small class of hardware bugs, significant gaps remain in their full potential for systematically addressing a wide range of hardware bugs. For instance, existing LLM methodologies struggle to detect bugs involving incorrect constant values, i.e., the use of wrong constants in source code. This limitation underscores the need for further exploration in utilizing LLMs to fully optimize the verification process. To bridge this gap, we propose a 3-phased 4-stage LLM-assisted systematic bug closure methodology that focuses on functional bugs in Verilog HDL rather than structural or syntactic issues. Our approach extracts functional properties of the DUV and systematically breaks down complex expressions into smaller sub-expressions to facilitate bug detection and correction. By employing RAG, the LLM is guided using the functional specifications and source code to identify and correct bugs. If the initial guidance through RAG is insufficient, our methodology initiates an iterative bug closure process. This includes incorporating more extensive information from the specifications, fetching additional lines of code for bug localization, and breaking down complex Verilog HDL expressions. In our comprehensive evaluation, we assess the LLM’s capabilities using 9 different categories of bugs. As benchmarks, we use 5 OpenTitan Intellectual Property (IP) cores to demonstrate the scalability and effectiveness of our bug closure methodology where ≈ 60% of the bugs were corrected. Specifically, we evaluate OpenAI’s GPT-4 in its ability to identify and correct functional bugs in Verilog HDL code.

This article explores various applications of artificial intelligence (AI) technologies in dairy farming, including the use of computer vision systems (CVS) for animal identification, BCS and body shape analysis, and potential uses of large language models (LLM) in the dairy industry. Among recent advancements in precision livestock farming tools, CVS have gained popularity as powerful solutions for individual animal monitoring. These systems can capture phenotypes from multiple animals simultaneously using a single device in an automated and nonintrusive manner. To match animals with their corresponding predicted phenotypes, these systems require individual animal identification, which can be achieved through external identification systems or computer vision-based animal identification algorithms. Additionally, modern natural language processing techniques, such as LLM, offer opportunities for advanced data integration, including unstructured textual data. Furthermore, we discuss the challenges associated with integrating data from different sources and modalities, such as images, text, and tabular data, into multimodal machine learning systems for phenotype prediction, which also represents a key area of AI application. Digital technologies such as CVS and LLM have the potential to transform dairy farming; CVS can provide individual and objective assessments of animal health, whereas LLM can integrate diverse data sources for phenotype prediction. Although there are many potential challenges ahead, these technologies offer significant opportunities for advancing animal health monitoring, farm management, and individual phenotyping.

PurposeThis study investigated the concordance of five different publicly available Large Language Models (LLM) with the recommendations of a multidisciplinary tumor board regarding treatment recommendations for complex breast cancer patient profiles.MethodsFive LLM, including three versions of ChatGPT (version 4 and 3.5, with data access until September 3021 and January 2022), Llama2, and Bard were prompted to produce treatment recommendations for 20 complex breast cancer patient profiles. LLM recommendations were compared to the recommendations of a multidisciplinary tumor board (gold standard), including surgical, endocrine and systemic treatment, radiotherapy, and genetic testing therapy options.ResultsGPT4 demonstrated the highest concordance (70.6%) for invasive breast cancer patient profiles, followed by GPT3.5 September 2021 (58.8%), GPT3.5 January 2022 (41.2%), Llama2 (35.3%) and Bard (23.5%). Including precancerous lesions of ductal carcinoma in situ, the identical ranking was reached with lower overall concordance for each LLM (GPT4 60.0%, GPT3.5 September 2021 50.0%, GPT3.5 January 2022 35.0%, Llama2 30.0%, Bard 20.0%). GPT4 achieved full concordance (100%) for radiotherapy. Lowest alignment was reached in recommending genetic testing, demonstrating a varying concordance (55.0% for GPT3.5 January 2022, Llama2 and Bard up to 85.0% for GPT4).ConclusionThis early feasibility study is the first to compare different LLM in breast cancer care with regard to changes in accuracy over time, i.e., with access to more data or through technological upgrades. Methodological advancement, i.e., the optimization of prompting techniques, and technological development, i.e., enabling data input control and secure data processing, are necessary in the preparation of large-scale and multicenter studies to provide evidence on their safe and reliable clinical application. At present, safe and evidenced use of LLM in clinical breast cancer care is not yet feasible.

Current advances in large language models (LLMs) and generative AI (GenAI) have produced both enthusiasm and concerns in the academic world, industry, and society in general. While optimistic views foresee unprecedented increase in efficiency and productivity, concerns have been expressed on the potential of these technologies to determine significant changes in most areas of human activity, which may not always have predictable or positive outcomes. One of the challenges often evoked in this context, not yet fully addressed, is the impact of the AI-powered agents on the educational sector, and especially on aspects such as student’s agency and control, creativity, and motivation in pedagogic activities that involve the use of this type of agents. The aim of the study is to address this question starting from the hypothesis that preliminary simulations of AI-based pedagogic scenarios can help instructors to better understand the inner mechanisms of these technologies and their possible impact on the learning, assignment completion and evaluation processes. The paper presents a set of experiments with simulated student-agent interactions generated by AI chatbots and proposes a formal framework for assessing this form of “imitation game” and its possible applications in real teaching-learning environments.