Awesome-Multimodal-Large-Language-Models

This repository is a curated list of research papers, datasets, and benchmarks focused on multimodal large language models — AI systems that can understand and reason about more than just text. Multimodal means the model can work with multiple types of input, most commonly combining text with images or video.

Standard large language models (LLMs) only process written language. Multimodal versions can also interpret what is in a photo, analyze a video, or listen to speech. The research area is moving fast and tracking which papers, models, and benchmarks exist is difficult.

This repository maintains a structured, frequently updated table of notable research papers organized by topic. Categories include multimodal instruction tuning (teaching models to follow instructions involving images), multimodal hallucination (when models incorrectly describe what they see), in-context learning (learning from examples shown in the prompt), chain-of-thought reasoning (having the model explain its visual reasoning step by step), and evaluation benchmarks for measuring how well models understand images and video.

The repository is maintained by a research group and also links to their own benchmark projects, including MME (for evaluating multimodal LLMs) and Video-MME (focused on video understanding). It also lists datasets used for training and evaluating these models.

You would use this as a research reference if you are working in the AI field and want to track progress in multimodal AI, or if you need to find relevant papers or datasets for a specific aspect of vision-language model development. The full README is longer than what was provided.