Obviously, a concept (which is an abstraction in more ways than one) is different from a textual representation. But LLMs don't operate on the textual description of a concept when they are doing their thing. A textual description (which is associated with other modalities in the training data) serves as an input format. LLMs perform non-linear transformations of points in their latent space. These transformations and representations are useful not only for generating text but also for controlling robots, for example (see VLAs in robotics).
> don't operate on the textual description of a concept when they are doing their thing.
It could be mapping the text to some other internal representation with connections to mappings from some other text/tokens. But it does not stop text from being the ground truth. It has nothing else going on!
The "hallucination" behavior alone should be enough to reject any claims that these are at least minimally similar to animal intelligence.
If LLMs' internal representations are essentially one-to-one mappings of input texts with no additional structure, how can those representations be useful for tasks like object manipulation in robotics?
How is transfer learning possible when non-textual training data enhances performance on textual tasks?
I didn't mean it is a one to one mapping from tokens. But instead it might be mapping a corpus of input text to some points in some multi dimensional space, (just like the input data a linear regression), then then it just extends the line further across that space to get the output.
>How is transfer learning possible when non-textual training data enhances performance on textual tasks?
If non-textual training data can be mapped to the same multi-dimensional space ( by using them alongside textual data during training or something like that), then shouldn't it be possible to do what you describe?
