Summary of Target Prompting For Information Extraction with Vision Language Model, by Dipankar Medhi
Target Prompting for Information Extraction with Vision Language Model
by Dipankar Medhi
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The recent advancements in Large Vision and Language (VLM) models have revolutionized information extraction systems, enabling state-of-the-art question-answering capabilities across various industries. VLMs excel in generating text from document images and providing accurate answers to questions. However, there are still challenges in effectively utilizing these models for precise conversational systems. Specifically, general prompting techniques used with large language models may not be suitable for vision language models, leading to ordinary output with information gaps. To overcome this limitation, the authors introduce Target prompting, a technique that explicitly targets specific regions of document images and generates related answers from those areas only. The paper also evaluates response quality using different user queries and input prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super smart computer system that can understand documents and answer questions. This is what’s happening with Large Vision and Language (VLM) models. They’re really good at taking pictures of documents and providing accurate answers to questions. But, there’s still some work to be done to make these systems even better. Right now, the way we ask questions doesn’t always get the best results from VLMs. To fix this, researchers are developing a new technique called Target prompting. It helps VLMs focus on specific parts of documents and answer questions based only on that information. The goal is to create a system that can have more natural conversations with humans. |
Keywords
» Artificial intelligence » Prompting » Question answering
