Summary of Iaa: Inner-adaptor Architecture Empowers Frozen Large Language Model with Multimodal Capabilities, by Bin Wang et al.
IAA: Inner-Adaptor Architecture Empowers Frozen Large Language Model with Multimodal Capabilities
by Bin Wang, Chunyu Xie, Dawei Leng, Yuhui Yin
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Inner-Adaptor Architecture (IAA) is a novel approach to multimodal large language models (MLLMs), addressing the common issue of fine-tuning language models with vision-language data, which can lead to performance degradation in natural language processing (NLP). By freezing the language model and incorporating multiple multimodal adaptors within the model, IAA enables the frozen language model to acquire multimodal capabilities without sacrificing NLP performance. This architecture is shown to outperform previous state-of-the-art methods on various vision-language benchmarks, while also achieving superior results on small-scale datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Inner-Adaptor Architecture is a new way to make large language models better at understanding images and text together. This is important because when we train these models with lots of image-text pairs, they often forget how to understand regular text. The IAA solves this problem by adding special parts to the model that help it connect with the text part, even if it’s not fully trained. This means the model can be good at understanding both text and images without losing its ability to understand just text. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Natural language processing » Nlp
