LLM image processing doesn’t work the same way reverse image lookup does.

Tldr explanation: Multimodal LLMs turn pictures into a thousand 200-500 or so words tokens, but reverse image lookups create perceptual hashes of images and look the hash of your uploaded image up in a database.

Much longer explanation:

Multimodal LLMs (technically, LMMs - large multimodal models) use vision transformers to turn images into tokens. They use tokens for words, too, but these tokens don’t also correspond to words. There are multiple ways this could be implemented, but a common approach is to break the image down into a grid, then transform each “patch” of a specific size, e.g., 16x16, into a single token. The patches aren’t transformed individually - the whole image is processed together, in context - but it still comes out of it with basically 200 or so tokens that allow it to respond to the image, the same way it would respond to text.

Current vision transformers also struggle with spatial awareness. They embed basic positional data into the tokens but it’s fragile and unsophisticated when it comes to spatial awareness. Fortunately there’s a lot to explore in that area so I’m sure there will continue to be improvements.

One example improvement, beyond improved spatial embeddings, would be to use a dynamic vision transformers that’s dependent on the context, or that can re-evaluate an image based off new information. Outside the use of vision transformers, simply training LMMs to use other tools on images when appropriate can potentially help with many of LMM image processing’s current shortcomings.

Given all that, asking an LLM to find the album for you is like - assuming you’ve given it the ability and permission to search the web - like showing the image to someone with no context, then them to help you find what music video - that they’ve never seen, by an artist whose appearance they describe with 10-20 generic words, none of which are their name - it’s in, and to hope there were, and that they remembered, the specific details that would make it would come up in the top ten results if searched for on Google. That’s a convoluted way to say that it’s a hard task.

By contrast, reverse image lookup basically uses a perceptual hash generated for each image. It’s the tool that should be used for your particular problem, because it’s well suited for it. LLMs were the hammer and this problem was a torx screw.

Suggesting you use - or better, using a reverse image lookup tool itself - is what the LLM should do in this instance. But it would need to have been trained to think to suggest this, capable of using a tool that could do the lookup, and have both access and permission to do the lookup.

Here’s a paper that might help understand the gaps between LMMs and tasks built for that specific purpose: https://arxiv.org/html/2305.07895v7