Abstract
Conversational AI has seen a growing interest among government, researchers, and industrialists. This comprehensive survey paper provides an in-depth analysis of large language models, specifically focusing on ChatGPT. This paper discusses the architecture, training process, and challenges associated with large language models, including bias, interpretability, and ethics. It explores various applications of ChatGPT and examines future research trends, such as improving model generalization, addressing data scarcity, and integrating multimodal capabilities. This survey also serves as a roadmap for researchers, practitioners, and policymakers, offering valuable insights into the current state and future potential of large language models and ChatGPT.
Author supplied keywords
- ChatGPT
- Large language models
- bias in language models
- conversational AI
- data scarcity
- deep learning
- dialogue systems
- ethics in AI
- generalization
- language generation
- language understanding
- model interpretability
- multimodal models
- natural language processing
- neural networks
- pre-training and fine-tuning
- sentiment analysis
- text classification
- text completion
- transformer models
Cite
CITATION STYLE
Hassija, V., Chakrabarti, A., Singh, A., Chamola, V., & Sikdar, B. (2023). Unleashing the Potential of Conversational AI: Amplifying Chat-GPT’s Capabilities and Tackling Technical Hurdles. IEEE Access, 11, 143657–143682. https://doi.org/10.1109/ACCESS.2023.3339553
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.