Advances in Large Language Model Empowered Machine Learning: Design and Application

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• large language models
• natural language processing
• computer vision
• vision-language learning
• machine learning