Yangyi-Chen/Multimodal-AND-Large-Language-Models

What it is: This is not a software project or a model, but a personal, curated collection of research papers. The author, Yangyi-Chen, maintains a list of academic papers they read from daily arXiv postings, focused on artificial intelligence (AI), language processing (CL), computer vision (CV), and machine learning (LG).

Purpose: The author created this list to record papers they personally find interesting or significant. The README note explains it is only for personal needs — the author subscribes to specific arXiv categories and tracks papers that catch their attention. Starting from June 2024, they decided to focus on papers that offer unique insights and substantial contributions.

Content: The project is essentially a huge, categorized bibliography. It lists paper titles, authors, and sometimes a short note (e.g., "Survey" or "Position Paper"). There are over 100 distinct topic categories, such as:

• Survey
• Position Paper
• Reasoning
• LLM Analysis
• Vision-Language Models
• Agents
• and many more

Each category contains a list of paper references. For example, under "Survey" you'll find papers like "Multimodal Learning with Transformers: A Survey" and "Sparks of Artificial General Intelligence: Early experiments with GPT-4." Under "Position Paper" there are entries like "GPT-4 Can’t Reason."

Organization: The README starts with a table of contents listing all categories. Then it presents each category as a section, with the papers listed bullet-style. Some entries include a brief annotation (e.g., "Procedure planning. Using text as weak supervision...") but most are just citation lines.

How to use it: It serves as a reference list for researchers or enthusiasts interested in these topics. You can browse the categories to find seminal or recent papers. The author invites readers to suggest missing important works.

Limitations: Because it is a personal list, it is not exhaustive — the author only covers the four arXiv subjects mentioned. Also, the list is selective, not a comprehensive survey.

In summary: This is a well-organized, manually maintained collection of AI research papers, useful for keeping up with current literature on multimodal and large language models.