Awesome-Multimodal-LLM
This article examines multimodal learning facilitated by large language models (LLMs), focusing on diverse modalities such as text, vision, and audio. It underscores the role of open-source, research-supportive LLM backbones like LLaMA, Alpaca, and Bloom and reviews various learning techniques including fine-tuning and in-context learning. Examples of models such as OpenFlamingo and MiniGPT-4 are discussed alongside evaluation methods like MultiInstruct and POPE. The article highlights key research advancements from 2021 to 2023, offering insights into projects enhancing LLM visual and language processing capabilities. It also provides resources and guidelines for contributors to encourage ongoing exploration and progress in LLM-guided multimodal learning.
