2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Год журнала: 2024, Номер 33, С. 24746 - 24755
Опубликована: Июнь 16, 2024
Язык: Английский
Natural Language Processing Journal, Год журнала: 2023, Номер 6, С. 100048 - 100048
Опубликована: Дек. 19, 2023
Large language models (LLMs) are a special class of pretrained (PLMs) obtained by scaling model size, pretraining corpus and computation. LLMs, because their large size on volumes text data, exhibit abilities which allow them to achieve remarkable performances without any task-specific training in many the natural processing tasks. The era LLMs started with OpenAI's GPT-3 model, popularity has increased exponentially after introduction like ChatGPT GPT4. We refer its successor OpenAI models, including GPT4, as family (GLLMs). With ever-rising GLLMs, especially research community, there is strong need for comprehensive survey summarizes recent progress multiple dimensions can guide community insightful future directions. start paper foundation concepts transformers, transfer learning, self-supervised models. then present brief overview GLLMs discuss various downstream tasks, specific domains languages. also data labelling augmentation robustness effectiveness evaluators, finally, conclude To summarize, this will serve good resource both academic industry people stay updated latest related GLLMs.
Язык: Английский
Процитировано
134
IEEE Journal of Biomedical and Health Informatics, Год журнала: 2023, Номер 27(12), С. 6074 - 6087
Опубликована: Сен. 22, 2023
Large AI models, or foundation are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people's imagination about far-reaching influence that have their potential to transform different domains our lives. In health informatics, advent brought new paradigms for design methodologies. The scale multi-modal data biomedical domain been ever-expanding especially since community embraced era deep learning, provides ground develop, validate, advance breakthroughs health-related areas. This article presents a comprehensive review from background applications. We identify seven key sectors applicable might substantial influence, including 1) bioinformatics; 2) medical diagnosis; 3) imaging; 4) informatics; 5) education; 6) public health; 7) robotics. examine challenges, followed by critical discussion future directions pitfalls transforming field informatics.
Язык: Английский
Процитировано
122
Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 233 - 249
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
6
International Journal of Medical Informatics, Год журнала: 2024, Номер 188, С. 105501 - 105501
Опубликована: Май 26, 2024
Recent enhancements in Large Language Models (LLMs) such as ChatGPT have exponentially increased user adoption. These models are accessible on mobile devices and support multimodal interactions, including conversations, code generation, patient image uploads, broadening their utility providing healthcare professionals with real-time for clinical decision-making. Nevertheless, many authors highlighted serious risks that may arise from the adoption of LLMs, principally related to safety alignment ethical guidelines. To address these challenges, we introduce a novel methodological approach designed assess specific feasibility adopting LLMs within area, focus nursing, evaluating performance thereby directing choice. Emphasizing LLMs' adherence scientific advancements, this prioritizes care personalization, according "Organization Economic Co-operation Development" frameworks responsible AI. Moreover, its dynamic nature is adapt future evolutions LLMs. Through integrating advanced multidisciplinary knowledge, Nursing Informatics, aided by prospective literature review, seven key domains evaluation items were identified follows: State Art Alignment & Safety. Focus, Accuracy Management Prompt Ambiguity. Data Integrity, Security, Ethics Sustainability, accordance OECD Recommendations Responsible Temporal Variability Responses (Consistency) Adaptation standardized terminology Classifications professionals. General Capabilities: Post User Feedback Self-Evolution Capability Organization Chapters. Ability Drive Evolution Healthcare. Nine state art evaluated using methodology oncology nursing decision-making, producing preliminary results. Gemini Advanced, Anthropic Claude 3 4 achieved minimum score Safety domain classification "recommended", being also endorsed across all domains. LLAMA 70B 3.5 classified "usable high caution." Others unusable domain. The identification recommended LLM combined critical, prudent, integrative use, can decision-making processes.
Язык: Английский
Процитировано
13
Machine Learning with Applications, Год журнала: 2025, Номер unknown, С. 100622 - 100622
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1
Computers & Security, Год журнала: 2025, Номер unknown, С. 104358 - 104358
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
1
Опубликована: Апрель 24, 2025
Язык: Английский
Процитировано
1
Communications in computer and information science, Год журнала: 2024, Номер unknown, С. 76 - 95
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
5
2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Год журнала: 2024, Номер unknown, С. 435 - 444
Опубликована: Март 12, 2024
Язык: Английский
Процитировано
4
Current Psychology, Год журнала: 2024, Номер unknown
Опубликована: Окт. 12, 2024
Язык: Английский
Процитировано
4