Summary of Exploring the Capabilities Of Large Multimodal Models on Dense Text, by Shuo Zhang et al.
Exploring the Capabilities of Large Multimodal Models on Dense Text
by Shuo Zhang, Biao Yang, Zhang Li, Zhiyin Ma, Yuliang Liu, Xiang Bai
First submitted to arxiv on: 9 May 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 This paper explores the capabilities of large multi-modal models (LMMs) in processing dense textual content. Dense text, often found in documents and product descriptions, carries important information that can inform better decisions. To evaluate LMMs’ strengths and weaknesses in complex text tasks, the authors propose a new dataset called DT-VQA, containing 170k question-answer pairs. The paper conducts a comprehensive evaluation of GPT4V, Gemini, and open-source LMMs on this dataset, highlighting their performance improvements through prompt engineering and downstream fine-tuning. The study aims to promote research on LMMs in dense text tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well computers can understand complex texts that have lots of information. These texts are often found in documents and product descriptions, and understanding them helps us make better decisions. To see how well computer models do this task, the researchers created a big dataset with questions and answers about these texts. They tested different computer models on this dataset and found ways to improve their performance. The goal is to help computers understand complex texts even better. |
Keywords
» Artificial intelligence » Fine tuning » Gemini » Multi modal » Prompt
