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Journal articles on the topic 'Large language model'

Author: Grafiati

Published: 29 July 2024

Last updated: 25 July 2025

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1

Kumar, Deepak, Dr Amandeep, Pinki Pinki, et al. "Visualization by Natural Language Processing and Large Language Model." International Journal of Research Publication and Reviews 6, no. 6 (2025): 11225–31. https://doi.org/10.55248/gengpi.6.0625.2340.

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Awotunde, Opeyemi Joseph. "Continuous Model Calibration: Leveraging Feedback-Driven Fine-Tuning for Self- Correcting Large Language Models." International Journal of Research Publication and Reviews 6, no. 3 (2025): 4145–58. https://doi.org/10.55248/gengpi.6.0325.1208.

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3

Lorenz, Kilian, Pascal Bürklin, Klemens Schnattinger, et al. "Refinetuning Decentralized Large Language Model for Privacy-Sensitive University Data." Journal of Robotics and Automation Research 6, no. 2 (2025): 01–11. https://doi.org/10.33140/jrar.06.02.01.

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This work focuses on refining a decentralized large language model (LLM) tailored for finetuning on privacy-sensitive university data. Devolved AI models, designed to operate across multiple distributed nodes, offer a promising solution for handling sensitive information by ensuring data remains localized at its source while collaboratively training a global model. The key challenge addressed in this study is the adaptation and fine-tuning of a decentralized LLM to work effectively with heterogeneous, privacyrestricted datasets typical in university environments, such as student records, resea

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B, Mr DHANUSH. "CHATBOT USING LARGE LANGUAGE MODEL." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34001.

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The concept of Natural Language Processing has seen a remarkable advancement in the recent years. This remarkable advancement was particularly with the development of Large Language Models (LLM). Large Language Models are used to develop a human like conversations. This LLM is a part of Natural Language Processing which focuses on enabling computers to understand, interpret, and generate human language. The existing system of chatbots does not generate human like responses. The proposed system of chatbots uses the power of Large Language Models to generate more human like responses, providing

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Zhang, Chengyi, Xingyu Wang, and Ziyun Wang. "Large language model in electrocatalysis." Chinese Journal of Catalysis 59 (April 2024): 7–14. http://dx.doi.org/10.1016/s1872-2067(23)64612-1.

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Salem, Nadia, Khawla Al-Tarawneh, Amjad Hudaib, et al. "Generating database schema from requirement specification based on natural language processing and large language model." Computer Research and Modeling 16, no. 7 (2024): 1703–13. https://doi.org/10.20537/2076-7633-2024-16-7-1703-1713.

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Sagi, Sriram. "Advancing AI: Enhancing Large Language Model Performance through GPU Optimization Techniques." International Journal of Science and Research (IJSR) 13, no. 3 (2024): 630–33. http://dx.doi.org/10.21275/sr24309100709.

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Lee, Youn-Kyoung. "Integrating the Large Language Model into L2 Pronunciation Teaching and Learning." Studies in Modern Grammar 125 (March 31, 2025): 129–46. https://doi.org/10.14342/smog.2025.125.129.

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Baral, Elina, and Sagar Shrestha. "Large Vocabulary Continuous Speech Recognition for Nepali Language." International Journal of Signal Processing Systems 8, no. 4 (2020): 68–73. http://dx.doi.org/10.18178/ijsps.8.4.68-73.

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Speech Recognition is a widely studied topic for high-resource languages like English and Mandarin. A plethora of publications exist that study the performance of several recognition methods for these languages. However differences in phonetics, accent, language model, etc between any two different languages demand for a study of speech recognition methodologies and components separately for each language. In this paper, we present a comparative study of popular speech recognition methods for Nepali, a low-resource Indo-Aryan language. We describe our approach to building the phonetic dictiona

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Lu, Zhouquan. "Large Language Models: Development in Model Scale and Challenges." Applied and Computational Engineering 114, no. 1 (2024): 154–61. https://doi.org/10.54254/2755-2721/2024.18276.

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Since the 1950s, language modeling (Language Models, LMs) has been one of the primary approaches for tasks such as machine translation, as well as language understanding and processing. It has been widely applied in the field of natural language processing (Natural Language Processing, NLP), significantly improving the performance of tasks related to natural language understanding and generation. In recent years, large language models (LLMs) have made remarkable advancements in technical architecture and model scale, providing strong technological support for NLP and other fields. This paper p

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MIYAGUNI, Masashi, Naoki TSUDA, Yusuke YATSU, and Kenichiro MATSUSHITA. "Business Use of Large Language Model." Journal of The Institute of Electrical Engineers of Japan 144, no. 10 (2024): 656–59. http://dx.doi.org/10.1541/ieejjournal.144.656.

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Huang, Sen, Kaixiang Yang, Sheng Qi, and Rui Wang. "When large language model meets optimization." Swarm and Evolutionary Computation 90 (October 2024): 101663. http://dx.doi.org/10.1016/j.swevo.2024.101663.

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13

Garg, Prerak, and Divya Beeram. "Large Language Model-Based Autonomous Agents." International Journal of Computer Trends and Technology 72, no. 5 (2024): 151–62. http://dx.doi.org/10.14445/22312803/ijctt-v72i5p118.

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14

Nguyen, Sophia, Beihao Zhou, Yi Ding, and Sihang Liu. "Towards Sustainable Large Language Model Serving." ACM SIGEnergy Energy Informatics Review 4, no. 5 (2024): 134–40. https://doi.org/10.1145/3727200.3727220.

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In this work, we study LLMs from a carbon emission perspective, addressing both operational and embodied emissions, and paving the way for sustainable LLM serving. We characterize the performance and energy of LLaMA with 1B, 3B, and 7B parameters using two Nvidia GPU types, a latest-generation RTX6000 Ada and an older-generation T4. We analytically model operational carbon emissions based on energy consumption and carbon intensities from three grid regions --- each representing a different energy source mix, and embodied carbon emissions based on chip area and memory size. Our characterization

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15

Maher, Gabriel. "LLMPC: Large Language Model Predictive Control." Computers 14, no. 3 (2025): 104. https://doi.org/10.3390/computers14030104.

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Recent advancements in planning prompting techniques for Large Language Models have improved their reasoning, planning, and action abilities. This paper develops a planning framework for Large Language Models using model predictive control that enables them to iteratively solve complex problems with long horizons. We show that in the model predictive control formulation, LLM planners act as approximate cost function optimizers and solve complex problems by breaking them down into smaller iterative steps. With our proposed planning framework, we demonstrate improved performance over few-shot pr

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K, Mr Muthukumaran, Velan S, Vaitheeshvar Alias Satyaprakash A T, Logeswaran S, and Kirubakar M. "Comprehensive Veterinary Health and Disease Management Assistance System using Large Language Model." International Journal of Research Publication and Reviews 5, no. 11 (2024): 6821–29. https://doi.org/10.55248/gengpi.5.1124.3414.

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Tsai, Ching-Chih, Jung-Chih Tsai, Huei-Min Lin, Yi-Qing Li, and Shih-Pang Tseng. "Applying a Large Language Model to Second Language Acquisition." Sensors and Materials 37, no. 7 (2025): 2743. https://doi.org/10.18494/sam5172.

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Zhu, Xunyu, Jian Li, Yong Liu, Can Ma, and Weiping Wang. "A Survey on Model Compression for Large Language Models." Transactions of the Association for Computational Linguistics 12 (2024): 1556–77. https://doi.org/10.1162/tacl_a_00704.

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Abstract Large Language Models (LLMs) have transformed natural language processing tasks successfully. Yet, their large size and high computational needs pose challenges for practical use, especially in resource-limited settings. Model compression has emerged as a key research area to address these challenges. This paper presents a survey of model compression techniques for LLMs. We cover methods like quantization, pruning, and knowledge distillation, highlighting recent advancements. We also discuss benchmarking strategies and evaluation metrics crucial for assessing compressed LLMs. This sur

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Liu, Yuxin. "Attention is All Large Language Model Need." ITM Web of Conferences 73 (2025): 02025. https://doi.org/10.1051/itmconf/20257302025.

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With the advent of the Transformer, the attention mechanism has been applied to Large Language Model (LLM), evolving from initial single- modal large models to today's multi-modal large models. This has greatly propelled the development of Artificial Intelligence (AI) and ushered humans into the era of large models. Single-modal large models can be broadly categorized into three types based on their application domains: Text LLM for Natural Language Processing (NLP), Image LLM for Computer Vision (CV), and Audio LLM for speech interaction. Multi-modal large models, on the other hand, can lever

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Schnitzer, Benjamin, Eveline Bader, and Giulio Crocco. "Das 4C Modell – Anwendungspotenziale von Large Language Models in innovativen Lernumgebungen zur beruflichen Schriftsprachförderung." Sprache im Beruf 7, no. 2 (2024): 218–31. http://dx.doi.org/10.25162/sprib-2024-0010.

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Watanabe, Masahiro, and Naoshi Uchihira. "Digital Business Model Analysis Using a Large Language Model." IIAI Letters on Business and Decision Science 4 (2024): 1. http://dx.doi.org/10.52731/lbds.v004.289.

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22

Shi, Zhouxing, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, and Cho-Jui Hsieh. "Red Teaming Language Model Detectors with Language Models." Transactions of the Association for Computational Linguistics 12 (2024): 174–89. http://dx.doi.org/10.1162/tacl_a_00639.

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Abstract The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent work has proposed algorithms to detect LLM-generated text and protect LLMs. In this paper, we investigate the robustness and reliability of these LLM detectors under adversarial attacks. We study two types of attack strategies: 1) replacing certain words in an LLM’s output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writ

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Goh, Ethan, Robert Gallo, Jason Hom, et al. "Large Language Model Influence on Diagnostic Reasoning." JAMA Network Open 7, no. 10 (2024): e2440969. http://dx.doi.org/10.1001/jamanetworkopen.2024.40969.

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ImportanceLarge language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves physician diagnostic reasoning.ObjectiveTo assess the effect of an LLM on physicians’ diagnostic reasoning compared with conventional resources.Design, Setting, and ParticipantsA single-blind randomized clinical trial was conducted from November 29 to December 29, 2023. Using remote video conferencing and in-person participation across multiple academic medical institutions, physicians

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Zhou, Wentao, Jinlin Wang, Longtao Zhu, Yi Wang, and Yulong Ji. "Flight Arrival Scheduling via Large Language Model." Aerospace 11, no. 10 (2024): 813. http://dx.doi.org/10.3390/aerospace11100813.

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The flight arrival scheduling problem is one of the critical tasks in air traffic operations, aiming to ensure that the flight arrive in the correct sequence safely. Existing methods primarily focus on the terminal area and often overlook the presence of training flight at the airport. Due to the limited generalization of traditional methods and varying control practices at different airports, training flight at airports still rely on manual control for arrival sorting. To effectively address these issues, we propose a novel method for slot allocation that leverages the strong reasoning capabi

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Aman, Mussa. "Large Language Model Based Fake News Detection." Procedia Computer Science 231 (2024): 740–45. http://dx.doi.org/10.1016/j.procs.2023.12.144.

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Zhang, Zhiping, Chenxinran Shen, Bingsheng Yao, Dakuo Wang, and Tianshi Li. "Secret Use of Large Language Model (LLM)." Proceedings of the ACM on Human-Computer Interaction 9, no. 2 (2025): 1–26. https://doi.org/10.1145/3711061.

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The advancements of Large Language Models (LLMs) have decentralized the responsibility for the transparency of AI usage. Specifically, LLM users are now encouraged or required to disclose the use of LLM-generated content for varied types of real-world tasks. However, an emerging phenomenon, users' secret use of LLM , raises challenges in ensuring end users adhere to the transparency requirement. Our study used mixed-methods with an exploratory survey (125 real-world secret use cases reported) and a controlled experiment among 300 users to investigate the contexts and causes behind the secret u

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27

Ren, Runtao, Jian Ma, and Jianxi Luo. "Large language model for patent concept generation." Advanced Engineering Informatics 65 (May 2025): 103301. https://doi.org/10.1016/j.aei.2025.103301.

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28

Marchis, Franck, and Ignacio G. López-Francos. "A Large Language Model for the Stars." Scientific American 34, no. 1s (2025): 110. https://doi.org/10.1038/scientificamerican032025-5tlgxvaghgnusdrwt5na1m.

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29

Singh, Dippu Kumar. "Unraveling Enterprise Large Language Model platform - Cohere." International Journal of Scientific and Research Publications 15, no. 2 (2025): 219–23. https://doi.org/10.29322/ijsrp.15.02.2025.p15823.

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30

RAJ, NEBIN K., ANIKHA D NAIR, SANJANA SS, Dr ARTHY M, and JOES ANTO. "Personalized Symptom Analysis using Large Language Model." International Journal of Advances in Engineering and Management 7, no. 2 (2025): 79–85. https://doi.org/10.35629/5252-07027985.

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The need for intelligent healthcare systems gained momentum in recent years, with advancements in AI and machine-learning technologies. This paper develops all-inclusive AIbased healthcare assistant predicting diseases based on the symptoms reported by the user and then providing personalized health recommendations. This hybrid model, that is integrated with the OpenAI language model, enables the system to analyze patient symptoms, then give detailed customized descriptions of diseases, dietary recommendations, medication advice, workout plans, and precautions. It also finds the nearest hospit

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Pardhi, Praful R., Saurabh Wagh, Gaurav Sharma, Abhash Goyal, and Pavan Pawar. "Enhancing Personalized Fitness: Integrating Large Language Model." EPJ Web of Conferences 328 (2025): 01021. https://doi.org/10.1051/epjconf/202532801021.

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This paper explores the integration of Large Language Models (LLMs) into workout planning and personal training to meet the growing demand for personalized fitness solutions. Traditional personal training, while effective, faces challenges in accessibility, scalability, and real-time adaptability. We propose a novel AI-powered approach using LLMs to address these limitations and enhance the training experience. Our methodology combines the natural language processing capabilities of LLMs with exercise science and nutrition principles. The system provides 24/7 personalized guidance, dynamic wor

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Chen, Xiaoyi, and Haixu Tang. "Designing a large language model for chemists." Patterns 6, no. 5 (2025): 101264. https://doi.org/10.1016/j.patter.2025.101264.

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Batsakis, Sotiris, Ilias Tachmazidis, Matthew Mantle, Nikolaos Papadakis, and Grigoris Antoniou. "Model Checking Using Large Language Models—Evaluation and Future Directions." Electronics 14, no. 2 (2025): 401. https://doi.org/10.3390/electronics14020401.

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Large language models (LLMs) such as ChatGPT have risen in prominence recently, leading to the need to analyze their strengths and limitations for various tasks. The objective of this work was to evaluate the performance of large language models for model checking, which is used extensively in various critical tasks such as software and hardware verification. A set of problems were proposed as a benchmark in this work and three LLMs (GPT-4, Claude, and Gemini) were evaluated with respect to their ability to solve these problems. The evaluation was conducted by comparing the responses of the th

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Ma, Ziyang, Guanrou Yang, Yifan Yang, et al. "Speech Recognition Meets Large Language Model: Benchmarking, Models, and Exploration." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24840–48. https://doi.org/10.1609/aaai.v39i23.34666.

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In this paper, we focus on prompting one of the most important tasks in the field of speech processing, i.e., automatic speech recognition (ASR), with speech foundation encoders and large language models (LLM). Despite the growing body of research in this area, we find that many crucial design decisions in LLM-based ASR systems are often inadequately justified. This lack of clarity impedes the field's progress, making it challenging to pinpoint which design choices truly improve model performance. To address these challenges, we conduct a comprehensive series of experiments that explore variou

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Zhang, Xieyun, Shimin Cai, Xiaorong Shen, Han Yang, Wenhao Hu, and Yanru Zhang. "Efficient unified information extraction model based on large language models." Applied Soft Computing 180 (August 2025): 113302. https://doi.org/10.1016/j.asoc.2025.113302.

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Singh, Pranaydeep, Orphée De Clercq, and Els Lefever. "Distilling Monolingual Models from Large Multilingual Transformers." Electronics 12, no. 4 (2023): 1022. http://dx.doi.org/10.3390/electronics12041022.

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Although language modeling has been trending upwards steadily, models available for low-resourced languages are limited to large multilingual models such as mBERT and XLM-RoBERTa, which come with significant overheads for deployment vis-à-vis their model size, inference speeds, etc. We attempt to tackle this problem by proposing a novel methodology to apply knowledge distillation techniques to filter language-specific information from a large multilingual model into a small, fast monolingual model that can often outperform the teacher model. We demonstrate the viability of this methodology on

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Nikhil, Pesati. "Security Considerations for Large Language Model Use: Implementation Research in Securing LLM-Integrated Applications." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 3 (2024): 19–27. https://doi.org/10.35940/ijrte.C8142.13030924.

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<strong>Abstract:</strong> Large Language Models (LLMs) are rapidly being adopted in various applications due to their natural language capabilities that enable user interaction using human language. As system designers, developers, and users embrace generative artificial intelligence and large language models in various applications, they need to understand the significant security risks associated with them. The paper describes a typical LLM-integrated application architecture and identifies multiple security risks to address while building these applications. In addition, the paper provides

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Oğul, İskender Ülgen, Fatih Soygazi, and Belgin Ergenç Bostanoğlu. "TurkMedNLI: a Turkish medical natural language inference dataset through large language model based translation." PeerJ Computer Science 11 (January 30, 2025): e2662. https://doi.org/10.7717/peerj-cs.2662.

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Natural language inference (NLI) is a subfield of natural language processing (NLP) that aims to identify the contextual relationship between premise and hypothesis sentences. While high-resource languages like English benefit from robust and rich NLI datasets, creating similar datasets for low-resource languages is challenging due to the cost and complexity of manual annotation. Although translation of existing datasets offers a practical solution, direct translation of domain-specific datasets presents unique challenges, particularly in handling abbreviations, metric conversions, and cultura

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Aukusti Laine, Timo. "Semantic Wave Functions: Exploring Meaning in Large Language Models through Quantum Formalism." Open Access Journal of Applied Science and Technology 3, no. 1 (2025): 01–22. https://doi.org/10.33140/oajast.03.01.11.

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Large Language Models (LLMs) encode semantic relationships in high-dimensional vector embeddings. This paper explores the analogy between LLM embedding spaces and quantum mechanics, positing that LLMs operate within a quantized semantic space where words and phrases behave as quantum states. To capture nuanced semantic interference effects, we extend the standard realvalued embedding space to the complex domain, drawing parallels to the double-slit experiment. We introduce a ”semantic wave function” to formalize this quantum-derived representation and utilize potential landscapes, such as the

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Beurer-Kellner, Luca, Marc Fischer, and Martin Vechev. "Prompting Is Programming: A Query Language for Large Language Models." Proceedings of the ACM on Programming Languages 7, PLDI (2023): 1946–69. http://dx.doi.org/10.1145/3591300.

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Large language models have demonstrated outstanding performance on a wide range of tasks such as question answering and code generation. On a high level, given an input, a language model can be used to automatically complete the sequence in a statistically-likely way. Based on this, users prompt these models with language instructions or examples, to implement a variety of downstream tasks. Advanced prompting methods can even imply interaction between the language model, a user, and external tools such as calculators. However, to obtain state-of-the-art performance or adapt language models for

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Alostad, Hana. "Large Language Models as Kuwaiti Annotators." Big Data and Cognitive Computing 9, no. 2 (2025): 33. https://doi.org/10.3390/bdcc9020033.

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Stance detection for low-resource languages, such as the Kuwaiti dialect, poses a significant challenge in natural language processing (NLP) due to the scarcity of annotated datasets and specialized tools. This study addresses these limitations by evaluating the effectiveness of open large language models (LLMs) in automating stance detection through zero-shot and few-shot prompt engineering, with a focus on the potential of open-source models to achieve performance levels comparable to those of closed-source alternatives. We also highlight the critical distinctions between zero- and few-shot

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Mahendra, Anton, and Styawati Styawati. "Implementasi Lowk-Rank Adaptation of Large Langauage Model (LoRA) Untuk Effisiensi Large Language Model." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 9, no. 4 (2024): 1881–90. https://doi.org/10.29100/jipi.v9i4.5519.

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Model transformator seperti LlaMA 2 sangat kuat untuk memproses berbagai tugas bahasa alami, namun memiliki kekuatan pemrosesan yang signifikan dan keterbatasan memori yang membuatnya sulit untuk diimplementasikan. Tantangan terbesarnya terletak pada konsumsi sumber daya penyimpanan yang besar dan kebutuhan daya komputasi dalam jumlah besar. Untuk mengatasi permasalahan tersebut, dikembangkan solusi berupa implementasi LoRA (Low Rank Adapter). LoRA, khususnya di LlaMA 2, menggunakan pendekatan adaptif dalam mengompresi model Transformer menggunakan adaptor berdaya rendah. Penerapan LoRA pada m

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Qarah, Faisal, and Tawfeeq Alsanoosy. "Evaluation of Arabic Large Language Models on Moroccan Dialect." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 22478–85. https://doi.org/10.48084/etasr.10331.

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Large Language Models (LLMs) have shown outstanding performance in many Natural Language Processing (NLP) tasks for high-resource languages, especially English, primarily because most of them were trained on widely available text resources. As a result, many low-resource languages, such as Arabic and African languages and their dialects, are not well studied, raising concerns about whether LLMs can perform fairly across them. Therefore, evaluating the performance of LLMs for low-resource languages and diverse dialects is crucial. This study investigated the performance of LLMs in Moroccan Arab

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Brijeshkumar Y. Panchal. "Examine the Opportunities and Challenges of Large Language Model (LLM) For Indic Languages." Journal of Information Systems Engineering and Management 10, no. 26s (2025): 301–25. https://doi.org/10.52783/jisem.v10i26s.4236.

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Large Language Models like GPT and BERT have made significant advancements in NLP, particularly in text generation, translation, and summarization. However, their application in Indic languages remains relatively unexplored due to unique linguistic challenges such as complex morphology, diverse scripts, and limited digitized resources. This systematic literature review follows PRISMA guidelines to identify, analyze, and evaluate existing research on the opportunities and challenges of LLMs for Indic languages. The review covers relevant publications from databases like Web of Science, IEEE Xpl

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Brijeshkumar Y. Panchal. "Examine the Opportunities and Challenges of Large Language Model (LLM) For Indic Languages." Journal of Information Systems Engineering and Management 9, no. 4s (2024): 1384–408. https://doi.org/10.52783/jisem.v9i4s.11887.

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Large Language Models like GPT and BERT have made significant advancements in NLP, particularly in text generation, translation, and summarization. However, their application in Indic languages remains relatively unexplored due to unique linguistic challenges such as complex morphology, diverse scripts, and limited digitized resources. This systematic literature review follows PRISMA guidelines to identify, analyze, and evaluate existing research on the opportunities and challenges of LLMs for Indic languages. The review covers relevant publications from databases like Web of Science, IEEE Xpl

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Kolenbrander, Jack, and Alan J. Michaels. "Personality Emulation Utilizing Large Language Models." Applied Sciences 15, no. 12 (2025): 6636. https://doi.org/10.3390/app15126636.

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Fake identities have proven to be an effective methodology for conducting privacy and cybersecurity research; however, existing models are limited in their ability to interact with and respond to received communications. To perform privacy research in more complex Internet domains, withstand enhanced scrutiny, and persist long-term, fake identities must be capable of automatically generating responses while maintaining consistent behavior and personality. This work proposes a method for assigning personality to fake identities using the widely accepted psychometric Big Five model. Leveraging t

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Tian, Shaohan, Xue Jiang, Weiren Wang, et al. "Steel design based on a large language model." Acta Materialia 285 (February 2025): 120663. https://doi.org/10.1016/j.actamat.2024.120663.

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Jayasri P G K, Jennifer Paul C T, Kajal S, and Gayathri R. "Mental Health Analysis System Using Large Language Model." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 12 (2024): 2729–33. https://doi.org/10.47392/irjaeh.2024.0377.

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Traditional mental health surveys in educational settings often provide a limited perspective on students' mental well-being, relying on standardized questions that fail to capture the complexity and individuality of each student's experience. To address this, a system leveraging Large Language Models (LLMs) has been developed to dynamically generate personalized and adaptive questions in real time, based on students' previous responses. This personalized approach fosters deeper engagement and provides a more nuanced understanding of students’ mental health. The system's adaptive nature ensure

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Hsueh, Nien-Lin, Hsuen-Jen Lin, and Lien-Chi Lai. "Applying Large Language Model to User Experience Testing." Electronics 13, no. 23 (2024): 4633. http://dx.doi.org/10.3390/electronics13234633.

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The maturation of internet usage environments has elevated User Experience (UX) to a critical factor in system success. However, traditional manual UX testing methods are hampered by subjectivity and lack of standardization, resulting in time-consuming and costly processes. This study explores the potential of Large Language Models (LLMs) to address these challenges by developing an automated UX testing tool. Our innovative approach integrates the Rapi web recording tool to capture user interaction data with the analytical capabilities of LLMs, utilizing Nielsen’s usability heuristics as evalu

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Johnson, Thomas F., Benno I. Simmons, Joseph Millard, et al. "Pressure to publish introduces large‐language model risks." Methods in Ecology and Evolution 15, no. 10 (2024): 1771–73. http://dx.doi.org/10.1111/2041-210x.14397.

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Abstract Large‐language models (LLMs) have the potential to accelerate research in ecology and evolution, cultivating new insights and innovation. However, whilst revelling in the plethora of opportunities, researchers need to consider that LLM use could also introduce risks. An important piece of context underpinning this perspective is the pressure to publish, where research careers are defined, at least partly, by publication metrics like number of papers, impact factor, citations etc. Coupled with academic employment insecurity, especially during early career, researchers may reason that L

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