Large Language Models: Their Success and Impact

Makridakis, Spyros; Petropoulos, Fotios; Kang, Yanfei

doi:10.3390/forecast5030030

Cited by 17 publications

(9 citation statements)

References 7 publications

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“…The number of researchers with equitable access to specialized expertise and resources in molecular biology and drug discovery is not satisfactory (Makridakis et al, 2023;Meyer et al, 2023). LLMs can overcome this gap by offering virtual access to expert knowledge and resources on the leading edge.…”

Section: Increasing Access To Specialized Expertise and Resourcesmentioning

confidence: 99%

Large language models reshaping molecular biology and drug development

Tripathi,

Gabriel,

Tripathi

et al. 2024

Chem Biol Drug Des

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The utilization of large language models (LLMs) has become a significant advancement in the domains of medicine and clinical informatics, providing a revolutionary potential for scientific breakthroughs and customized therapies. LLM models are trained on large datasets and exhibit the capacity to comprehend and analyze intricate biological data, encompassing genomic sequences, protein structures, and clinical health records. With the utilization of their comprehension of the language of biology, they possess the ability to reveal concealed patterns and insights that may evade human researchers. LLMs have been shown to positively impact various aspects of molecular biology, including the following: genomic analysis, drug development, precision medicine, biomarker development, experimental design, collaborative research, and accessibility to specialized expertise. However, it is imperative to acknowledge and tackle the obstacles and ethical implications involved. The careful consideration of data bias and generalization, data privacy and security, explainability and interpretability, and ethical concerns around responsible application is vital. The successful resolution of these obstacles will enable us to fully utilize the capabilities of LLMs, leading to substantial progress in the fields of molecular biology and pharmaceutical research. This progression also has the ability to bolster influential impacts for both the individual and the broader community.

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Section: Increasing Access To Specialized Expertise and Resourcesmentioning

confidence: 99%

Large language models reshaping molecular biology and drug development

Tripathi,

Gabriel,

Tripathi

et al. 2024

Chem Biol Drug Des

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“…Context understanding remains a critical issue, as LLMs and ChatGPT/GPT-4 may struggle to grasp complex queries fully, resulting in responses that lack nuance and accuracy. Additionally, emotional intelligence, scalability, and the preference for human interactions represent further challenges in delivering seamless customer service experiences [409].…”

Section: G Marketingmentioning

confidence: 99%

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

Hadi,

tashi,

Qureshi

et al. 2023

Preprint

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<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent and contextually fitting responses. Large language models (LLMs) are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, including natural language processing (NLP), machine translation, and question-answering. This survey paper provides a comprehensive overview of LLMs, including their history, architecture, training methods, applications, and challenges. The paper begins by discussing the fundamental concepts of generative AI and the architecture of generative pre- trained transformers (GPT). It then provides an overview of the history of LLMs, their evolution over time, and the different training methods that have been used to train them. The paper then discusses the wide range of applications of LLMs, including medical, education, finance, and engineering. It also discusses how LLMs are shaping the future of AI and how they can be used to solve real-world problems. The paper then discusses the challenges associated with deploying LLMs in real-world scenarios, including ethical considerations, model biases, interpretability, and computational resource requirements. It also highlights techniques for enhancing the robustness and controllability of LLMs, and addressing bias, fairness, and generation quality issues. Finally, the paper concludes by highlighting the future of LLM research and the challenges that need to be addressed in order to make LLMs more reliable and useful. This survey paper is intended to provide researchers, practitioners, and enthusiasts with a comprehensive understanding of LLMs, their evolution, applications, and challenges. By consolidating the state-of-the-art knowledge in the field, this survey serves as a valuable resource for further advancements in the development and utilization of LLMs for a wide range of real-world applications. The GitHub repo for this project is available at https://github.com/anas-zafar/LLM-Survey</p>

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“…Several technological innovations, in particular, have played a crucial role in this change [3], including blockchain technology [4], the metaverse [5], [6], emerging technologies [7], [8], and artificial intelligence (AI) [9]. One example is generative AI technologies, such as large language models [10][11][12]. The presence of AI, such as ChatGPT, has revolutionized how we process information, communicate, and learn [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Generative AI as Virtual Healthcare Assistant for Enhancing Patient Care Quality

Samala,

Rawas

2024

Int. J. Onl. Eng.

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This study investigates the potential of Chat Generative Pre-Trained Transformer (ChatGPT) as a virtual healthcare assistant to enhance the quality of patient care. Inadequate patient care within healthcare systems is a key issue that has resulted in lower satisfaction and medical errors. Virtual healthcare assistants, exemplified by ChatGPT, have emerged as a promising solution to mitigate these challenges. A comprehensive literature review compares the benefits and drawbacks of using virtual healthcare assistants with those of human healthcare providers to assess their effectiveness in enhancing patient care. The article discusses the ChatGPT development process, including the data sources used, training and validation, and the integration of this technology into healthcare systems. The results of testing ChatGPT in patient care, including patient feedback, are provided. The study interprets these findings and indicates that ChatGPT can significantly enhance patient care. The implications of implementing virtual healthcare assistants in the healthcare sector are also explored, along with potential future research areas for enhancing ChatGPT. This study provides important new insights into how virtual healthcare assistants might enhance patient care and offers recommendations for healthcare organizations and legislators on leveraging ChatGPT. It shows that the astonishing development in patient care, known as ChatGPT, has the potential to revolutionize the healthcare industry.

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Large Language Models: Their Success and Impact

Cited by 17 publications

References 7 publications

Large language models reshaping molecular biology and drug development

Large language models reshaping molecular biology and drug development

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

Generative AI as Virtual Healthcare Assistant for Enhancing Patient Care Quality

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