Summary of Declarative Knowledge Distillation From Large Language Models For Visual Question Answering Datasets, by Thomas Eiter et al.
Declarative Knowledge Distillation from Large Language Models for Visual Question Answering Datasets
by Thomas Eiter, Jan Hadl, Nelson Higuera, Johannes Oetsch
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 novel approach to Visual Question Answering (VQA) is presented, focusing on declarative reasoning and interpretability. The method leverages Large Language Models (LLMs) to extend initial theory on VQA reasoning, guided by examples from prominent datasets like CLEVR and GQA. This knowledge distillation process enables the creation of modular solutions with clear advantages in interpretability over end-to-end trained systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to answer a question about an image. It’s not just looking at the picture; you need to understand what it shows and use that information to find the right answer. This is called Visual Question Answering, or VQA for short. Some ways of doing this are better than others because they’re easier to understand. But making those methods work can be tricky. Our solution uses special language models to help create these methods. We tested it on two big datasets and showed that it works well. |
Keywords
» Artificial intelligence » Knowledge distillation » Question answering
