Summary of Image First or Text First? Optimising the Sequencing Of Modalities in Large Language Model Prompting and Reasoning Tasks, by Grant Wardle and Teo Susnjak
Image First or Text First? Optimising the Sequencing of Modalities in Large Language Model Prompting and Reasoning Tasks
by Grant Wardle, Teo Susnjak
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 This paper investigates how the ordering of images and text within multi-modal prompts affects the performance of large language models (LLMs) in reasoning tasks. Empirical evaluations using three commercial LLMs showed that modality sequencing can significantly impact accuracy, particularly for simpler tasks involving a single image. However, in more complex tasks requiring multiple images and intricate reasoning steps, the effect of sequencing diminished due to increased cognitive demands. The study highlights the importance of question/prompt structure, revealing that modality sequencing plays a crucial role in nested and multi-step reasoning tasks. While LLMs excel in initial reasoning stages, they struggle to re-incorporate earlier information, underscoring challenges of multi-hop reasoning within transformer architectures. These findings offer valuable insights for improving multi-modal prompt design, with implications across fields such as education, medical imaging, and cross-modal learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how the order of images and text affects how well large language models do on thinking tasks. The researchers tested three big language models and found that the order matters a lot for simple tasks, but not so much for harder ones. They also found that how you structure your question or prompt is important, especially when you’re trying to get the model to think through complex ideas. Even though the models are really good at starting to solve problems, they struggle to use information from earlier on in their thinking process. This research can help us make better prompts and improve how well language models do on different tasks. |
Keywords
» Artificial intelligence » Multi modal » Prompt » Transformer
