[red75prime]

The latest models are mostly LMMs (large multimodal models). If a model builds an internal representation that integrates all the modalities we are dealing with (robotics even provides tactile inputs), it becomes harder and harder to imagine why those representations should be qualitatively different.

[qsera]

It can't, simply because the textual description of a concept is different from the concept itself.

[red75prime]

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).

[qsera]

> 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.

[ajam1507]

> The "hallucination" behavior alone should be enough to reject any claims that these are at least minimally similar to animal intelligence.

Can you elaborate on why you think this is the case?

[red75prime]

The internal representation happen to be useful not only for outputting text. What does it mean from your standpoint?

[qsera]

I didn't understand. Can you clarify?

[red75prime]

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?