[mdp2021]

It is explicitly stated in the paper that

> One may argue that LLMs are implicitly learning a hierarchical representation, but we stipulate that models with an explicit hierarchical architecture are better suited to create coherent long-form output

And the problem remains that (text surrounding the above):

> Despite the undeniable success of LLMs and continued progress, all current LLMs miss a crucial characteristic of human intelligence: explicit reasoning and planning at multiple levels of abstraction. The human brain does not operate at the word level only. We usually have a top-down process to solve a complex task or compose a long document: we first plan at a higher level the overall structure, and then step-by-step, add details at lower levels of abstraction. [...] Imagine a researcher giving a fifteen-minute talk. In such a situation, researchers do not usually prepare detailed speeches by writing out every single word they will pronounce. Instead, they outline a flow of higher-level ideas they want to communicate. Should they give the same talk multiple times, the actual words being spoken may differ, the talk could even be given in different languages, but the flow of higher-level abstract ideas will remain the same. Similarly, when writing a research paper or essay on a specific topic, humans usually start by preparing an outline that structures the whole document into sections, which they then refine iteratively. Humans also detect and remember dependencies between the different parts of a longer document at an abstract level. If we expand on our previous research writing example, keeping track of dependencies means that we need to provide results for each of the experiment mentioned in the introduction. Finally, when processing and analyzing information, humans rarely consider every single word in a large document. Instead, we use a hierarchical approach: we remember which part of a long document we should search to find a specific piece of information. To the best of our knowledge, this explicit hierarchical structure of information processing and generation, at an abstract level, independent of any instantiation in a particular language or modality, cannot be found in any of the current LLMs

[motoboi]

I suppose humans need high level concepts because we can only hold 7[] things in working memory. Computers don’t have that limitation.

Also, humans cannot iterate over thousands of possibilities in a second, like computers do.

And finally, animal brains are severely limited by heat dissipation and energy input flow.

Based on that, artificial intelligence may arise from unexpected simple strategies, given the fundamental differences in scale and structure from animal brains.

- where 7 is whatever number is the correct number nowadays.

[dr_dshiv]

I just don’t understand that — I thought deep neural nets were inherently hierarchical. Or at least emergently hierarchical?

[mdp2021]

Neural Nets can be made to be hierarchical - I would say a most notable example is the Convolutional Neural Network so successfully promoted by Yann Le Cun.

But the issue with the LLMs architectures in place is with the idea of "predicting the next token", so strident with the exercise of intelligence - where we search instead for the "neighbouring fitting ideas".

So, "hierarchical" in this context is there to express that it is typical of natural intelligence to refine an idea - formulating an hypothesis and improving its form (hence its expression) step after step of pondering. The issue of transparency in current LLMs, and the idea of "predicting the next token", do not help in having the idea of typical natural intelligence mechanism and the tentative interpretation of LLM internals match.

[nightski]

Is that true? There are many attention/mlp layers stacked on top of each other. Higher level layers aren't performing attention on input tokens, but instead on the output of the previous layer.

[mdp2021]

> Is that true

Well, if you are referring to «The issue of transparency in current LLMs», I have not read an essay that explains satisfactorily the inner process and world modelling inside LLMs. Some pieces say (guess?) that the engine has no idea what the whole concept in the reply would be before outputting all the tokens, others swear it seems impossible it has no such idea before formulation...

[throwawaymaths]

there is a way that "predicting the next token" is ~append-only turing machine. Obviously the tokens we're using might be suboptimal for whatever goalpost "agi" is at any given time, but the structure/strategies of LLMs is probably not far from a really good one, modulo refactoring for efficiency like MAMBA (but still doing token stream prediction, esp. during inference)

[motoboi]

Not necessarily.

For visual tasks, that is the state of the art, with visual features being "gouped" into more semantically relevant parts ("circles" grouped into "fluffy textures" grouped into "dog ears"). This hierarchy building behavior is baked into the model.

For transformers, not so much. Although each transformer block output serve as input for the next block, they can learn hierarchical relationship (in latent space, not in human language), but that is not backed nor enforced in the architecture.