Advances in Large Language Model Empowered Machine Learning: Design and Application
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 10 December 2024 | Viewed by 9112
Special Issue Editors
Interests: natural language processing; computer vision; vision-language learning
Interests: natural language processing
Interests: vision and language; video understanding
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The field of artificial intelligence (AI) has witnessed a monumental transformation with the advent of large language models (LLMs). LLM techniques, such as ChatGPT, GPT-4, Llama, Flamingo, Blip-2, etc., have displayed remarkable capabilities in natural language processing (NLP), computer vision (CV), and many other intelligence-related tasks. The breakthroughs achieved by LLMs have not only revolutionized the way we interact with machines but also opened up exciting avenues for empowering various machine learning applications. LLMs have proven to be remarkably effective in understanding and processing varied data, such as language, vision, and video, making them a powerful tool for tackling complex problems in a wide range of disciplines, including but not limited to healthcare, finance, education, marketing, and social sciences.
The focus of this Special Issue is to explore the cutting-edge developments in LLM-empowered machine learning, with a particular emphasis on both the design aspects of LLMs and their broad application across diverse domains, such as NLP and CV. We welcome submissions (both of original research papers and review articles) related, but not limited to, the following topics:
- LLM-empowered machine learning:
- In-context learning;
- Chain-of-thought reasoning;
- Content creation;
- Data analysis and understanding;
- Knowledge-base/graph enhanced reliable generation.
- LLM-empowered NLP:
- Summarization and text generation;
- Information extraction;
- Question answering;
- Sentiment analysis and opinion mining;
- Semantic parsing;
- Machine translation;
- Recommendation.
- LLM-empowered multimodal learning:
- Text-to-image generation;
- Text-to-video generation;
- Image/video captioning;
- 3D understanding;
- Multimodal information retrieval;
- Multimodal question answering;
- Multimodal fusion and integration of information;
- Multimodal applications/pipelines.
We look forward to receiving your contributions.
Dr. Hao Fei
Dr. Fei Li
Dr. Wei Ji
Guest Editors
Manuscript Submission Information
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Keywords
- large language models
- natural language processing
- computer vision
- vision-language learning
- machine learning
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