Have you ever tried to train a Large Language Model (LLM) to extract data from an image, only to find it struggling to accurately identify checked boxes in a grid? You’re not alone. I’ve been exploring this challenge and wanted to share my thoughts on how to improve the process.
The issue arises when an LLM is presented with an image containing a grid of habits, some of which are checked to indicate completion. The AI system often gets confused, marking days as completed when they’re not, and vice versa. It’s frustrating, especially when you know the data is right there, hidden in plain sight.
So, what can we do to make this process more accurate? I believe the solution lies in a combination of design tweaks, image post-processing, and clever prompting. For instance, we could use a more intuitive grid design, making it easier for the LLM to distinguish between checked and unchecked boxes. Alternatively, we could apply image processing techniques to enhance the image quality and reduce noise. Finally, we could refine our prompting strategy to provide more context and guidance for the LLM.
I’d love to hear from others who have tackled this challenge. What strategies have you found to be effective in extracting data from images with LLMs?