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Tag: arabic
Bibliography items where occurs: 17
- The AI Index 2022 Annual Report / 2205.03468 / ISBN:https://doi.org/10.48550/arXiv.2205.03468 / Published by ArXiv / on (web) Publishing site
- Chapter 3 Technical AI Ethics
- A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics / 2310.05694 / ISBN:https://doi.org/10.48550/arXiv.2310.05694 / Published by ArXiv / on (web) Publishing site
- 3. From PLMs to LLMs for healthcare
References - Revolutionizing Customer Interactions: Insights and Challenges in Deploying ChatGPT and Generative Chatbots for FAQs / 2311.09976 / ISBN:https://doi.org/10.48550/arXiv.2311.09976 / Published by ArXiv / on (web) Publishing site
- 3. Chatbot approaches overview: Taxonomy of existing methods
References - Evaluation Ethics of LLMs in Legal Domain / 2403.11152 / ISBN:https://doi.org/10.48550/arXiv.2403.11152 / Published by ArXiv / on (web) Publishing site
- 3 Method
- Detecting AI Generated Text Based on NLP and Machine Learning Approaches / 2404.10032 / ISBN:https://doi.org/10.48550/arXiv.2404.10032 / Published by ArXiv / on (web) Publishing site
- II. Literature Review
- A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs) / 2405.03066 / ISBN:https://doi.org/10.48550/arXiv.2405.03066 / Published by ArXiv / on (web) Publishing site
- References
- Cyber Risks of Machine Translation Critical Errors : Arabic Mental Health Tweets as a Case Study / 2405.11668 / ISBN:https://doi.org/10.48550/arXiv.2405.11668 / Published by ArXiv / on (web) Publishing site
- Abstract
1. Introduction
2.MT Critical Errors
3. Data Compiling and Annotation
4.Error Analysis
6. Conclusion
7. Bibliographical References - Mapping the individual, social, and biospheric impacts of Foundation Models / 2407.17129 / ISBN:https://doi.org/10.48550/arXiv.2407.17129 / Published by ArXiv / on (web) Publishing site
- References
- The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources / 2406.16746 / ISBN:https://doi.org/10.48550/arXiv.2406.16746 / Published by ArXiv / on (web) Publishing site
- 3 Data Sources
- Data-Centric Foundation Models in Computational Healthcare: A Survey / 2401.02458 / ISBN:https://doi.org/10.48550/arXiv.2401.02458 / Published by ArXiv / on (web) Publishing site
- 8 Performance Evaluation
- XTRUST: On the Multilingual Trustworthiness of Large Language Models / 2409.15762 / ISBN:https://doi.org/10.48550/arXiv.2409.15762 / Published by ArXiv / on (web) Publishing site
- Abstract
1 Introduction
3 XTRUST Construction
4 Experiments
Appendices - Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models
/ 2410.12880 / ISBN:https://doi.org/10.48550/arXiv.2410.12880 / Published by ArXiv / on (web) Publishing site
- 2 Previous studies
4 Cultural safety dataset
5 Experimental setup
6 Main results on evaluation set
8 Results after cultural safeguarding - Data Defenses Against Large Language Models / 2410.13138 / ISBN:https://doi.org/10.48550/arXiv.2410.13138 / Published by ArXiv / on (web) Publishing site
- References
- Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models
/ 2410.12880 / ISBN:https://doi.org/10.48550/arXiv.2410.12880 / Published by ArXiv / on (web) Publishing site
- Appendices
- Clio: Privacy-Preserving Insights into Real-World AI Use / 2412.13678 / ISBN:https://doi.org/10.48550/arXiv.2412.13678 / Published by ArXiv / on (web) Publishing site
- Appendices
- Deploying Privacy Guardrails for LLMs: A Comparative Analysis of Real-World Applications
/ 2501.12456 / ISBN:https://doi.org/10.48550/arXiv. / Published by ArXiv / on (web) Publishing site
- State of the Art
Deployment 1: Data and Model Factory - A Critical Field Guide for Working with Machine Learning Datasets / 2501.15491 / ISBN:https://doi.org/10.48550/arXiv. / Published by ArXiv / on (web) Publishing site
- 4. Types of Datasets
References - Safety at Scale: A Comprehensive Survey of Large Model Safety / 2502.05206 / ISBN:https://doi.org/10.48550/arXiv. / Published by ArXiv / on (web) Publishing site
- 6 Diffusion Model Safety