Summary of Wiki-llava: Hierarchical Retrieval-augmented Generation For Multimodal Llms, by Davide Caffagni et al.
Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs
by Davide Caffagni, Federico Cocchi, Nicholas Moratelli, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM)
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 paper proposes a novel approach to endowing multimodal language models (LLMs) with the capability to answer questions that require external knowledge. The method, called Wiki-LLaVA, integrates an external knowledge source of multimodal documents through a hierarchical retrieval pipeline. This enables the LLM to retrieve relevant passages from the external source and use them as additional context for generating more effective and precise dialogues. The paper demonstrates the effectiveness of this approach on datasets tailored for visual question answering with external data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new way to make language models smarter by giving them access to more information. They call it Wiki-LLaVA, which stands for “Wiki-based Language Learning and Vision-and-Language Adapters”. This method allows the model to find relevant answers from a big library of documents and use that information to create better conversations. The team tested their approach on special datasets designed for asking questions about pictures and found that it worked really well. |
Keywords
» Artificial intelligence » Question answering
