Summary of A Review Of Multi-modal Large Language and Vision Models, by Kilian Carolan and Laura Fennelly and Alan F. Smeaton
A Review of Multi-Modal Large Language and Vision Models
by Kilian Carolan, Laura Fennelly, Alan F. Smeaton
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent surge in Large Language Models (LLMs) has led to advancements in text understanding and generation capabilities. The extension of LLMs to multi-modal large language models (MM-LLMs) enables processing of image, video, and audio information, opening up applications like text-to-video generation, image captioning, and text-to-speech synthesis. This paper reviews the current state of LLMs with multi-modal capabilities and MM-LLMs, tracing their development from transformer-based architectures like OpenAI’s GPT series and Google’s BERT to attention mechanisms enhancing model performance. The review covers major LLMs and MM-LLMs, including techniques for model tuning, fine-tuning, and prompt engineering. Additionally, the paper analyzes ethical considerations, such as data bias and model misuse, emphasizing responsible AI development and deployment. The implications of open-source versus proprietary models in AI research are also discussed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent advancements in Large Language Models (LLMs) have led to significant improvements in text understanding and generation capabilities. This has opened up new possibilities for applications like text-to-video generation, image captioning, and text-to-speech synthesis. In this paper, the authors review the current state of LLMs with multi-modal capabilities and MM-LLMs. They discuss how these models can be used to improve various tasks and applications. |
Keywords
» Artificial intelligence » Attention » Bert » Fine tuning » Gpt » Image captioning » Multi modal » Prompt » Transformer
