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Journal articles on the topic 'Generative Large Language Model'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Long, Xinwei, Jiali Zeng, Fandong Meng, et al. "Generative Multi-Modal Knowledge Retrieval with Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 18733–41. http://dx.doi.org/10.1609/aaai.v38i17.29837.

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Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications. However, existing methods face challenges in terms of their effectiveness and training efficiency, especially when it comes to training and integrating multiple retrievers to handle multi-modal queries. In this paper, we propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases, even when trained with limited data.

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GOU, NYUHUAN. "Research on the Translation Principles Based on Generative Large Language Models." International Journal of Novel Research in Interdisciplinary Studies 11, no. 4 (2024): 1–8. https://doi.org/10.5281/zenodo.13143304.

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<strong>Abstract:</strong> This study delves into the core principles and applications of generative large language models (LLMs) in translation technology, analyzing how these models achieve high-precision and high-efficiency automatic translation with their superior language generation capabilities and deep learning architecture. By examining the structure, training methods, and optimization strategies of generative LLMs, we uncover their mechanisms in handling language mapping, contextual information, and complex linguistic phenomena. The research demonstrates that appropriate model selecti

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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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Li, Xiaoxi, Yujia Zhou, and Zhicheng Dou. "UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8688–96. http://dx.doi.org/10.1609/aaai.v38i8.28714.

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Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in natural language processing. Existing methods for GDR and GAR rely on separate retrieval and reader modules, which hinder simultaneous optimization. To overcome this, we present UniGen, a Unified Generative framework for retrieval and question answering that integrates both tasks into a single generative model leveraging the capabilities of large language models. UniGen employs a shared encoder and two distinct decoders

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Han, Haixia, Jiaqing Liang, Jie Shi, Qianyu He, and Yanghua Xiao. "Small Language Model Can Self-Correct." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 18162–70. http://dx.doi.org/10.1609/aaai.v38i16.29774.

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Generative Language Models (LMs) such as ChatGPT have exhibited remarkable performance across various downstream tasks. Nevertheless, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone. Previous studies have devised sophisticated pipelines and prompts to induce large LMs to exhibit the capability for self-correction. However, large LMs are explicitly prompted to verify and modify their answers separately rather than completing all steps spontaneously like humans. Moreover, these complex prompts are extremely challenging for small LMs to fo

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G, Ananya. "RAG based Chatbot using LLMs." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35600.

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Historically, Artificial Intelligence (AI) was used to understand and recommend information. Now, Generative AI can also help us create new content. Generative AI builds on existing technologies, like Large Language Models (LLMs) which are trained on large amounts of text and learn to predict the next word in a sentence. Generative AI can not only create new text, but also images, videos, or audio. This project focuses on the implementation of a chatbot based the concepts of Generative AI and Large Language Models which can answer any query regarding the content provided in the PDFs. The prima

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Kundu, Subhasis. "Redefining Software Development: Fine-Tuning Generative AI and Large Language Models for Intelligent Automation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 02 (2023): 1–9. https://doi.org/10.55041/ijsrem17792.

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This study explores the transformative impact of Generative AI and Large Language Models (LLMs) on software development by leveraging intelligent automation. It delves into sophisticated methods for refining LLMs to enhance code generation, improve adaptive learning abilities, and support autonomous software engineering processes [1] [2]. This study investigates how these technologies can be integrated into current development workflows to tackle issues such as code quality, scalability, and ethical concerns. Innovative strategies to boost model performance have been introduced, such as target

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Yan, Xueming, Yan Xiao, and Yaochu Jin. "Generative Large Language Models Explained [AI-eXplained]." IEEE Computational Intelligence Magazine 19, no. 4 (2024): 45–46. http://dx.doi.org/10.1109/mci.2024.3431454.

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Zhang, Zhengyan, Xu Han, Hao Zhou, et al. "CPM: A large-scale generative Chinese Pre-trained language model." AI Open 2 (2021): 93–99. http://dx.doi.org/10.1016/j.aiopen.2021.07.001.

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Zhao, Yichong, Kenta Oono, Hiroki Takizawa, and Masaaki Kotera. "GenerRNA: A generative pre-trained language model for de novo RNA design." PLOS ONE 19, no. 10 (2024): e0310814. http://dx.doi.org/10.1371/journal.pone.0310814.

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The design of RNA plays a crucial role in developing RNA vaccines, nucleic acid therapeutics, and innovative biotechnological tools. However, existing techniques frequently lack versatility across various tasks and are dependent on pre-defined secondary structure or other prior knowledge. To address these limitations, we introduce GenerRNA, a Transformer-based model inspired by the success of large language models (LLMs) in protein and molecule generation. GenerRNA is pre-trained on large-scale RNA sequences and capable of generating novel RNA sequences with stable secondary structures, while

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Ross, Angela, Kathleen McGrow, Degui Zhi, and Laila Rasmy. "Foundation Models, Generative AI, and Large Language Models." CIN: Computers, Informatics, Nursing 42, no. 5 (2024): 377–87. http://dx.doi.org/10.1097/cin.0000000000001149.

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We are in a booming era of artificial intelligence, particularly with the increased availability of technologies that can help generate content, such as ChatGPT. Healthcare institutions are discussing or have started utilizing these innovative technologies within their workflow. Major electronic health record vendors have begun to leverage large language models to process and analyze vast amounts of clinical natural language text, performing a wide range of tasks in healthcare settings to help alleviate clinicians' burden. Although such technologies can be helpful in applications such as patie

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Joshi, Himanshu, and Volkan Ustun. "Augmenting Cognitive Architectures with Large Language Models." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 281–85. http://dx.doi.org/10.1609/aaaiss.v2i1.27689.

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A particular fusion of generative models and cognitive architectures is discussed with the help of the Soar and Sigma cognitive architectures. After a brief introduction to cognitive architecture concepts and Large Language Models as exemplar generative AI models, one approach towards their fusion is discussed. This is then analyzed with a summary of potential benefits and extensions needed to existing cognitive architecture that is closest to the proposal.

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Bhargava, Kumar, Kumar Tejaswini, and Nadakuditi Swapna. "Ethical Implications of Generative AI: The Case of Large Language Models." Journal of Scientific and Engineering Research 10, no. 7 (2023): 122–27. https://doi.org/10.5281/zenodo.12666901.

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Generative artificial intelligence (GenAI), including influential large language models (LLMs) such as GPT-3, has significantly advanced fields like healthcare, education, and customer service by generating human-like text and more. However, these advancements also present significant ethical challenges. Critical concerns include the perpetuation of societal biases, privacy risks associated with the extensive use of data, potential misuse in creating deepfakes and misinformation, and the opaque decision-making processes of these models. Moreover, the impact of GenAI on employment raises questi

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Feretzakis, Georgios, Konstantinos Papaspyridis, Aris Gkoulalas-Divanis, and Vassilios S. Verykios. "Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative Review." Information 15, no. 11 (2024): 697. http://dx.doi.org/10.3390/info15110697.

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Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, whi

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Researcher. "UNBINDING THE POTENTIAL OF LARGE LANGUAGE MODELS IN GENERATIVE AI." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 784–93. https://doi.org/10.5281/zenodo.13912309.

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LLMs, or large language models, have created new opportunities for natural language processing and conversation production, transforming the area of Generative Artificial Intelligence (AI). This study explores LLMs' capabilities, obstacles, and innovative possibilities related to Generative AI applications, demonstrating their revolutionary influence. We explore how major developments in LLM technologies, including Transformer, BERT, and GPT-3 architectures, have transformed text production, conversation quality, and context-aware answers. We examine maximizing performance and boosting output

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Cobb, Peter J. "Large Language Models and Generative AI, Oh My!" Advances in Archaeological Practice 11, no. 3 (2023): 363–69. http://dx.doi.org/10.1017/aap.2023.20.

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OverviewWe have all read the headlines heralding, often hyperbolically, the latest advances in text- and image-based Artificial Intelligence (AI). What is perhaps most unique about these developments is that they now make relatively good AI accessible to the average Internet user. These new services respond to human prompts, written in natural language, with generated output that appears to satisfy the prompt. Consequently, they are categorized under the term “generative AI,” whether they are generating text, images, or other media. They work by modeling human language statistically, to “learn

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Brie, Paul, Nicolas Burny, Arthur Sluÿters, and Jean Vanderdonckt. "Evaluating a Large Language Model on Searching for GUI Layouts." Proceedings of the ACM on Human-Computer Interaction 7, EICS (2023): 1–37. http://dx.doi.org/10.1145/3593230.

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The field of generative artificial intelligence has seen significant advancements in recent years with the advent of large language models, which have shown impressive results in software engineering tasks but not yet in engineering user interfaces. Thus, we raise a specific research question: would an LLM-based system be able to search for relevant GUI layouts? To address this question, we conducted a controlled study evaluating how Instigator, an LLM-based system for searching GUI layouts of web pages by generative pre-trained training, would return GUI layouts that are relevant to a given i

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Prashant, Kaushik. "Evaluating Next Generation NLG Models for Graph to Text Generation with Fine-Tuning Vs Generalized Model for Longer Generated Texts." Evaluating Next Generation NLG Models for Graph to Text Generation with Fine-Tuning Vs Generalized Model for Longer Generated Texts 9, no. 1 (2024): 5. https://doi.org/10.5281/zenodo.10597748.

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The paper investigates the feasibility of generative models for graph-to-text generation tasks, particularly in a zero-shot setting where no fine-tuning or additional training resources are utilized. The study evaluates the performance of GPT-3 and ChatGPT on graph-to-text datasets, comparing their results with those of fine-tuned language model (LLM) models like T5 and BART. The findings reveal that generative models, specifically GPT-3 and ChatGPT, exhibit the ability to produce fluent and coherent text, with notable BLEU scores of 11.07 and 11.18 on the AGENDA, & WebNLG datasets, respec

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Tuan, Nguyen Trung, Philip Moore, Dat Ha Vu Thanh, and Hai Van Pham. "A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications." Applied Sciences 14, no. 7 (2024): 3036. http://dx.doi.org/10.3390/app14073036.

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ChatGPT plays significant roles in the third decade of the 21st Century. Smart cities applications can be integrated with ChatGPT in various fields. This research proposes an approach for developing large language models using generative artificial intelligence models suitable for small- and medium-sized enterprises with limited hardware resources. There are many generative AI systems in operation and in development. However, the technological, human, and financial resources required to develop generative AI systems are impractical for small- and medium-sized enterprises. In this study, we pre

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J, Akarsh. "Language Enabled Image Originator." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34747.

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Traditionally, forensic artists would painstakingly sketch a suspect's face from a witness's statement in order to create forensic photographs. There are restrictions on this procedure, though. First, it depends a great deal on the interpretation of the artist, which can bring falsehoods and prejudices. It can also take a lot of time, particularly if the drawing needs to be refined repeatedly. Image generation is the process of creating new pictures that are comparable to the people in a certain dataset. Producing visually realistic pictures that fit the input's properties is the aim of image

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Zyda, Michael. "Large Language Models and Generative AI, Oh My!" Computer 57, no. 3 (2024): 127–32. http://dx.doi.org/10.1109/mc.2024.3350290.

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Ruchika, Ruchika. "A Review of Current Concerns and Mitigation Strategies on Generative AI and LLMs." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem03936.

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Abstract The upcoming of the large language models and generative artificial intelligence had Completely change the way in which we generate and understand language, and also start the beginning of a new phase in AI-driven applications. This review paper over see the advancements and changes that have occurred over time, providing a thorough assessment of generative artificial intelligence and large language models, while we also look upon their impactful potential across different areas. The first section of the research focuses on the changes of extensive language models and generative AI, a

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Ruchika, Ruchika. "A Review of Current Concerns and Mitigation Strategies on Generative AI and LLMs." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem03927.

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Abstract The upcoming of the large language models and generative artificial intelligence had Completely change the way in which we generate and understand language, and also start the beginning of a new phase in AI-driven applications. This review paper over see the advancements and changes that have occurred over time, providing a thorough assessment of generative artificial intelligence and large language models, while we also look upon their impactful potential across different areas. The first section of the research focuses on the changes of extensive language models and generative AI, a

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Gupta, Sagar. "Retrieval-Augmented Generation and Hallucination in Large Language Models: A Scholarly Overview." Scholars Journal of Engineering and Technology 13, no. 05 (2025): 328–30. https://doi.org/10.36347/sjet.2025.v13i05.003.

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Large Language Models (LLMs) have revolutionized natural language processing tasks, yet they often suffer from "hallucination” the confident generation of factually incorrect information. Retrieval-Augmented Generation (RAG) has emerged as a promising technique to mitigate hallucinations by grounding model responses in external documents. This article explores the underlying causes of hallucinations in LLMs, the mechanisms and architectures of RAG systems, their effectiveness in reducing hallucinations, and ongoing challenges. We conclude with a discussion of future directions for integrating

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Guo, Jianyu, Jingnan Chen, Li Ren, Huanlai Zhou, Wenbo Xu, and Haitao Jia. "Constructing Chinese taxonomy trees from understanding and generative pretrained language models." PeerJ Computer Science 10 (October 3, 2024): e2358. http://dx.doi.org/10.7717/peerj-cs.2358.

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The construction of hypernym taxonomic trees, a critical task in the field of natural language processing, involves extracting lexical relationships, specifically creating a tree structure that represents hypernym relationships among a given set of words within the same domain. In this work, we present a method for constructing hypernym taxonomy trees in the Chinese language domain, and we named it CHRRM (Chinese Hypernym Relationship Reasoning Model). Our method consists of two main steps: First, we utilize pre-trained models to predict hypernym relationships between pairs of words; second, w

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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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Lee, Byungkyu. "Generative Agent-Based Models Powered by Large Language Models." Korean Journal of Sociology 58, no. 4 (2024): 151–88. https://doi.org/10.21562/kjs.2024.11.58.4.151.

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Meng, Chutian, Fan Ma, Jiaxu Miao, Chi Zhang, Yi Yang, and Yueting Zhuang. "Image Regeneration: Evaluating Text-to-Image Model via Generating Identical Image with Multimodal Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6090–98. https://doi.org/10.1609/aaai.v39i6.32651.

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Diffusion models have revitalized the image generation domain, playing crucial roles in both academic research and artistic expression. With the emergence of new diffusion models, assessing the performance of text-to-image models has become increasingly important. Current metrics focus on directly matching the input text with the generated image, but due to cross-modal information asymmetry, this leads to unreliable or incomplete assessment results. Motivated by this, we introduce the Image Regeneration task in this study to assess text-to-image models by tasking the T2I model with generating

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Rahbar Alam, Md, Prakash Anand, Rishika Barve, Abhrajit Chakraborty, and Madhu D Naik. "Multilingual Text to Video Generation of Press Information Bureau Press Release." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42380.

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This paper introduces a novel AI-powered framework that turns the Press Information Bureau (PIB) press releases into interactive, multilingual video content. The proposed system automates the summarization of text from press releases into 13 regional languages, along with creating video from that summary by using Generative Adversarial Networks and Large language model. This makes it accessible, culturally relevant, and allows better communication and interaction. Preliminary results show higher outreach and interaction metrics, thereby making the solution presented here quite promising for an

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Zhou, Jinfeng, Yongkang Huang, Bosi Wen, et al. "CharacterBench: Benchmarking Character Customization of Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 26101–10. https://doi.org/10.1609/aaai.v39i24.34806.

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Character-based dialogue (aka role-playing) enables users to freely customize characters for interaction, which often relies on LLMs, raising the need to evaluate LLMs’ character customization capability. However, existing benchmarks fail to ensure a robust evaluation as they often only involve a single character category or evaluate limited dimensions. Moreover, the sparsity of character features in responses makes feature-focused generative evaluation both ineffective and inefficient. To address these issues, we propose CharacterBench, the largest bilingual generative benchmark, with 22,859

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Yang, Chi Bok, and Yang Sok Kim. "Implementation of Retrieval Augmented Generation (RAG) Model Using LLM: A RapidMiner-Based Approach." Korean Institute of Smart Media 14, no. 2 (2025): 34–42. https://doi.org/10.30693/smj.2025.14.2.34.

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Generative AI technology, driven by Large Language Models (LLMs), is being increasingly utilized to overcome existing limitations. Retrieval-Augmented Generation (RAG) has emerged as an effective approach to reduce hallucination in LLMs by leveraging up-to-date and domain-specific knowledge beyond training data. However, most studies propose programming-based implementations. This research introduces a GUI-based RAG framework using RapidMiner, to construct RAG systems without programming proficiency. The methodology includes storing and retrieving embeddings with the Qdrant vector database and

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P, Sai Gyaneshwar Chary, Rahul Pasha Shaik, Sandeep Sirra, and Vishwa Shanthi M. "TEXT TO IMAGE GENERATION IN PYTHON USING IMAGEN MODEL AND STREAMLIT." TEXT TO IMAGE GENERATION IN PYTHON USING IMAGEN MODEL AND STREAMLIT 2 2, M. Vishwa Shanthi (2023): 54. https://doi.org/10.5281/zenodo.7868137.

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Text-to-image generation is a fascinating application of computer vision and natural language processing, where the goal is to generate realistic and diverse images based on textual descriptions. In this project, we propose a text-to-image generation system using Python programming language and two main libraries, Imagen and Streamlit. The system consists of a generative adversarial network (GAN) model trained on a large dataset of images and their corresponding captions, and a text processing and generation module. 

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Zholus, Artem, Maksim Kuznetsov, Roman Schutski, et al. "BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 26083–91. https://doi.org/10.1609/aaai.v39i24.34804.

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Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding of the complex physical interactions between the molecule and its environment. This paper presents a novel generative model, BindGPT, which uses a conceptually simple but powerful approach to create 3D molecules within the protein's binding site. Our model produces molecular graphs and conformations jointly, eliminating the need for an extra graph reconstruction step. We pre-train BindGPT on a large-scale dataset and fine-tune it with reinforcement learnin

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Khruschak, S. V., O. М. Tkachenko, and I. S. Kolesnyk. "RAG efficiency improvement for building intellectual scientific knownledge databases." Optoelectronic Information-Power Technologies 49, no. 1 (2025): 89–97. https://doi.org/10.31649/1681-7893-2025-49-1-89-97.

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The article describes the development of an intellectual knowledge base based on scientific articles using large language models in the mode of generation by augmented search. Various methods of increasing the relevance of the sample of cited sources and generated answers of the language model and the choice of approaches to building language generative systems taking into account the specifics of scientific materials in Ukrainian and English are investigated. The use of different language models for generating answers is also considered. In the course of the study, a set of criteria for a com

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Muskaan Goyal and Pranav Bhasin. "Beyond the model: Key differentiators in large language models and multi-agent services." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 2703–6. https://doi.org/10.30574/wjarr.2025.26.1.1295.

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With the launch of foundation models like DeepSeek, Manus AI, and Llama 4, it has become evident that large language models (LLMs) are no longer the sole defining factor in generative AI. As many now operate at comparable levels of capability, the real race is not about having the biggest model but optimizing the surrounding ecosystem, including data quality and management, computational efficiency, latency, and evaluation frameworks. This review article delves into these critical differentiators that ensure modern AI services are efficient and profitable.

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Khaliq, Ayesha, Sikander Bakht Abbasi, Arslan Ilyas, Saim Masood Shaikh, and Syed Ashar Ali. "Optimizing Academic Queries With Retrieval-Augmented Large Language Models." Migration Letters 21, S10 (2024): 1274–83. https://doi.org/10.59670/ml.v21is10.11858.

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This research investigates the application of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) methods to enhance the management of academic queries, providing advantages for both students and educators. The study aims to improve the precision and pertinence of generated answers by combining LLMs with multi-source RAG systems. The model employs PDF datasets of various sizes and incorporates vector database support to streamline storage and retrieval, thereby boosting the model's capacity to handle extensive datasets. To process and produce comprehensive responses, the rese

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Pahune, Saurabh, and Noopur Rewatkar. "Healthcare: A Growing Role for Large Language Models and Generative AI." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 2288–301. http://dx.doi.org/10.22214/ijraset.2023.55573.

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Abstract: Large language models and generative artificial intelligence (GAI) have recently demonstrated significant promise for revolutionizing a range of industries, including healthcare. The paper investigates how these cutting-edge AI developments are transforming healthcare applications. We focus on how big language models, like GPT-3 (generative pretrained transformer), Visual ChatGPT and generative AI, such Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), can be applied to solve important problems in the healthcare sector. Medical text analysis is one of the ma

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Guțu, Bogdan Mihai, and Nirvana Popescu. "Exploring Data Analysis Methods in Generative Models: From Fine-Tuning to RAG Implementation." Computers 13, no. 12 (2024): 327. https://doi.org/10.3390/computers13120327.

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The exponential growth in data from technological advancements has created opportunities across fields like healthcare, finance, and social media, but sensitive data raise security and privacy challenges. Generative models offer solutions by modeling complex data and generating synthetic data, making them useful for the analysis of large private datasets. This article is a review of data analysis techniques based on generative models, with a focus on large language models (LLMs). It covers the strengths, limitations, and applications of methods like the fine-tuning of LLMs and retrieval-augmen

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Chaka, Chaka. "Currently Available GenAI-Powered Large Language Models and Low-Resource Languages: Any Offerings? Wait Until You See." International Journal of Learning, Teaching and Educational Research 23, no. 12 (2024): 148–73. https://doi.org/10.26803/ijlter.23.12.9.

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A lot of hype has accompanied the increasing number of generative artificial intelligence-powered large language models (LLMs). Similarly, much has been written about what currently available LLMs can and cannot do, including their benefits and risks, especially in higher education. However, few use cases have investigated the performance and generative capabilities of LLMs in low-resource languages. With this in mind, one of the purposes of the current study was to explore the extent to which seven, currently available, free-to-use versions of LLMs (ChatGPT, Claude, Copilot, Gemini, GroqChat,

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Pesati, Nikhil. "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. http://dx.doi.org/10.35940/ijrte.c8142.13030924.

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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 guidance on potential miti

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Dehal, Ramandeep Singh, Mehak Sharma, and Enayat Rajabi. "Knowledge Graphs and Their Reciprocal Relationship with Large Language Models." Machine Learning and Knowledge Extraction 7, no. 2 (2025): 38. https://doi.org/10.3390/make7020038.

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The reciprocal relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs) highlights their synergistic potential in enhancing artificial intelligence (AI) applications. LLMs, with their natural language understanding and generative capabilities, support the automation of KG construction through entity recognition, relation extraction, and schema generation. Conversely, KGs serve as structured and interpretable data sources that improve the transparency, factual consistency and reliability of LLM-based applications, mitigating challenges such as hallucinations and lack of expl

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Chu, Timothy, Zhao Song, and Chiwun Yang. "How to Protect Copyright Data in Optimization of Large Language Models?" Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 17871–79. http://dx.doi.org/10.1609/aaai.v38i16.29741.

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Large language models (LLMs) and generative AI have played a transformative role in computer research and applications. Controversy has arisen as to whether these models output copyrighted data, which can occur if the data the models are trained on is copyrighted. LLMs are built on the transformer neural network architecture, which in turn relies on a mathematical computation called Attention that uses the softmax function. In this paper, we observe that large language model training and optimization can be seen as a softmax regression problem. We then establish a method of efficiently perform

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Dvorak, Claire, and Paweena Phornprapha. "Assessment of Generative AI Large Language Model Knowledge in Children's Nutrition Education." Current Developments in Nutrition 8 (July 2024): 102770. http://dx.doi.org/10.1016/j.cdnut.2024.102770.

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Cao, Zhenjie, Zhuo Deng, Jie Ma, Jintao Hu, and Lan Ma. "MammoVLM: A generative large vision–language model for mammography-related diagnostic assistance." Information Fusion 118 (June 2025): 102998. https://doi.org/10.1016/j.inffus.2025.102998.

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Li, Haoran, Yulin Chen, Zihao Zheng, et al. "Simulate and Eliminate: Revoke Backdoors for Generative Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 397–405. https://doi.org/10.1609/aaai.v39i1.32018.

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With rapid advances, generative large language models (LLMs) dominate various Natural Language Processing (NLP) tasks from understanding to reasoning. Yet, language models' inherent vulnerabilities may be exacerbated due to increased accessibility and unrestricted model training on massive data. A malicious adversary may publish poisoned data online and conduct backdoor attacks on the victim LLMs pre-trained on the poisoned data. Backdoored LLMs behave innocuously for normal queries and generate harmful responses when the backdoor trigger is activated. Despite significant efforts paid to LLMs'

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Head, Cari Beth, Paul Jasper, Matthew McConnachie, Linda Raftree, and Grace Higdon. "Large language model applications for evaluation: Opportunities and ethical implications." New Directions for Evaluation 2023, no. 178-179 (2023): 33–46. http://dx.doi.org/10.1002/ev.20556.

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AbstractLarge language models (LLMs) are a type of generative artificial intelligence (AI) designed to produce text‐based content. LLMs use deep learning techniques and massively large data sets to understand, summarize, generate, and predict new text. LLMs caught the public eye in early 2023 when ChatGPT (the first consumer facing LLM) was released. LLM technologies are driven by recent advances in deep‐learning AI techniques, where language models are trained on extremely large text data from the internet and then re‐used for downstream tasks with limited fine‐tuning required. They offer exc

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Nafar, Aliakbar, Kristen Brent Venable, and Parisa Kordjamshidi. "Reasoning over Uncertain Text by Generative Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24911–20. https://doi.org/10.1609/aaai.v39i23.34674.

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This paper considers the challenges Large Language Models (LLMs) face when reasoning over text that includes information involving uncertainty explicitly quantified via probability values. This type of reasoning is relevant to a variety of contexts ranging from everyday conversations to medical decision-making. Despite improvements in the mathematical reasoning capabilities of LLMs, they still exhibit significant difficulties when it comes to probabilistic reasoning. To deal with this problem, we introduce the Bayesian Linguistic Inference Dataset (BLInD), a new dataset specifically designed t

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Alam, Sirojul, Jaka Abdul Jabar, Fauzi Abdurrachman, Bambang Suharjo, and H. A. Danang Rimbawa. "Improving Large Language Model’s Ability to Find the Words Relationship." Jurnal Bumigora Information Technology (BITe) 6, no. 2 (2024): 141–48. https://doi.org/10.30812/bite.v6i2.4127.

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Background: It is still possible to enhance the capabilities of popular and widely used large language models (LLMs) such as Generative Pre-trained Transformer (GPT). Using the Retrieval-Augmented Generation (RAG) architecture is one method of achieving enhancement. This architectural approach incorporates outside data into the model to improve LLM capabilities. Objective: The aim of this research is to prove that the RAG can help LLMs respond with greater precision and rationale. Method: The method used in this work is utilizing Huggingface Application Programming Interface (API) for word emb

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Waterfall, Heidi R., Ben Sandbank, Luca Onnis, and Shimon Edelman. "An empirical generative framework for computational modeling of language acquisition." Journal of Child Language 37, no. 3 (2010): 671–703. http://dx.doi.org/10.1017/s0305000910000024.

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ABSTRACTThis paper reports progress in developing a computer model of language acquisition in the form of (1) a generative grammar that is (2) algorithmically learnable from realistic corpus data, (3) viable in its large-scale quantitative performance and (4) psychologically real. First, we describe new algorithmic methods for unsupervised learning of generative grammars from raw CHILDES data and give an account of the generative performance of the acquired grammars. Next, we summarize findings from recent longitudinal and experimental work that suggests how certain statistically prominent str

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Valavandan, Ramamurthy, Prakash Valavandan, Kanagalakshmi S, et al. "Exploring the Role of Large Language Models (LLMs) and Generative AI in Dietary Management of Sinusitis." International Journal of Research Publication and Reviews 5, no. 1 (2024): 4569–79. http://dx.doi.org/10.55248/gengpi.5.0124.0338.

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