6 ms·

This feels like a failure to learn the bitter lesson: You're just taking the translation to concepts that the LLM is certainly already doing and trying to make

This feels like a failure to learn the bitter lesson: You're just taking the translation to concepts that the LLM is certainly already doing and trying to make it explicitly forced.

mdp2021 2y ago
That should be proven. The two approaches - predicting tokens vs predicting "sentences" - should be compared to see how much their output differ in terms of quality. Edit2: ...and both (and their variants) be compared to other ideas such as "multi-token prediction"... Edit: or, appropriateness of the approach should be demonstrated after acquired "transparency" of how the LLMs effectively internally work. I am not aware of studies that make the inner workings of LLMs adequately clear. Edit3: Substantially, the architecture should be as solid as possible (and results should reflect that).
- blackeyeblitzar 2y ago
  Isn’t “sentence prediction” roughly the same as multi token prediction of sufficient length? In the end are we just talking about a change to hyper parameters or maybe a new hyper parameter that controls the granularity of “prediction length”?
  - mdp2021 2y ago
    > multi token prediction of sufficient length Is multi token prediction the same as predicting the embedding of a complex token (the articulation of those input tokens in a sentence)?
    - blackeyeblitzar 2y ago
      To be honest I don’t know. Maybe the only way to know is to build and measure all these variations.
anon373839 2y ago
The bitter lesson isn’t a law of nature, though. And as GPT-style LLMs appear to be at the foot of a scaling wall, I personally think inductive bias is due for a comeback.
- Der_Einzige 2y ago
  Everyone keeps claiming this but we have zero evidence of any kind of scaling wall what-so-ever. Oh you mean data? Synthetic Data, Agents, and Digitization solve that.
  - UltraSane 2y ago
    There seems to be a affordable scaling wall.
  - anon373839 2y ago
    I disagree, but I also wasn’t referring to the exhaustion of training materials. I am referring to the fact that exponentially more compute is required to achieve linear gains in performance. At some point, it just won’t be feasible to do $50B training runs, you know?
    - throw5959 2y ago
      50B still seems reasonable compared to the revenue of the Big AI companies.
      - mentalgear 2y ago
        what revenues? If by big AI companies you mean llm service providers (OpenAI, ...), their revenues are far from high or profitable. https://www.cnbc.com/2024/09/27/openai-sees-5-billion-loss-this-year-on-3point7-billion-in-revenue.html https://www.cnbc.com/2024/09/27/openai-sees-5-billion-loss-t... Maybe Nvidia, but they are a chip / hardware maker first. And even for them 50B training run with no exponential gains seems unreasonable. Better to optimize the architecture / approach first, which also is what most companies are doing now before scaling out.
mdp2021 2y ago
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 2y ago
  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.
Jensson 2y ago
> You're just taking the translation to concepts that the LLM is certainly already doing and trying to make it explicitly forced. That is what tokens are doing in the first place though, and you get better results with tokens instead of letters.
- mdp2021 2y ago
  Well, individual letters in these languages in use* do not convey specific meaning, while individual tokens do - so, you cannot really construe a ladder that would go from letter to token, then from token to sentence. This said, to research whether the search for concepts (in the solutions space) works better than the search for tokens seems absolutely dutiful, in absence of a solid theory that showed otherwise. (*Sounds convey their own meaning e.g. in proto-Indo-European according to some interpretations, but that becomes too remote in the current descendants - you cannot reconstruct the implicit sound-token in words directly in English, just from the spelling.)
- IanCal 2y ago
  Is that true? I thought there was a desire to move towards byte level work rather than tokens, and that the benefits of tokens was more that you are reducing the context size for the same input.
  - fngjdflmdflg 2y ago
    >there was a desire to move towards byte level work rather than tokens Yeah, latest work on this is from Meta a last month.[0] It showed good results. [0] https://ai.meta.com/research/publications/byte-latent-transformer-patches-scale-better-than-tokens/ https://ai.meta.com/research/publications/byte-latent-transf... (https://news.ycombinator.com/item?id=42415122 https://news.ycombinator.com/item?id=42415122)
blurbleblurble 2y ago
At a performance boost of 10-100x :)

This feels like a failure to learn the bitter lesson: You're just taking the translation to concepts that the LLM is certainly already doing and trying to make it explicitly forced.

mdp2021 2y ago

That should be proven. The two approaches - predicting tokens vs predicting "sentences" - should be compared to see how much their output differ in terms of quality. Edit2: ...and both (and their variants) be compared to other ideas such as "multi-token prediction"... Edit: or, appropriateness of the approach should be demonstrated after acquired "transparency" of how the LLMs effectively internally work. I am not aware of studies that make the inner workings of LLMs adequately clear. Edit3: Substantially, the architecture should be as solid as possible (and results should reflect that).

blackeyeblitzar 2y ago
Isn’t “sentence prediction” roughly the same as multi token prediction of sufficient length? In the end are we just talking about a change to hyper parameters or maybe a new hyper parameter that controls the granularity of “prediction length”?
- mdp2021 2y ago
  > multi token prediction of sufficient length Is multi token prediction the same as predicting the embedding of a complex token (the articulation of those input tokens in a sentence)?
  - blackeyeblitzar 2y ago
    To be honest I don’t know. Maybe the only way to know is to build and measure all these variations.

anon373839 2y ago

The bitter lesson isn’t a law of nature, though. And as GPT-style LLMs appear to be at the foot of a scaling wall, I personally think inductive bias is due for a comeback.

Der_Einzige 2y ago
Everyone keeps claiming this but we have zero evidence of any kind of scaling wall what-so-ever. Oh you mean data? Synthetic Data, Agents, and Digitization solve that.
- UltraSane 2y ago
  There seems to be a affordable scaling wall.
- anon373839 2y ago
  I disagree, but I also wasn’t referring to the exhaustion of training materials. I am referring to the fact that exponentially more compute is required to achieve linear gains in performance. At some point, it just won’t be feasible to do $50B training runs, you know?
  - throw5959 2y ago
    50B still seems reasonable compared to the revenue of the Big AI companies.
    - mentalgear 2y ago
      what revenues? If by big AI companies you mean llm service providers (OpenAI, ...), their revenues are far from high or profitable. https://www.cnbc.com/2024/09/27/openai-sees-5-billion-loss-this-year-on-3point7-billion-in-revenue.html https://www.cnbc.com/2024/09/27/openai-sees-5-billion-loss-t... Maybe Nvidia, but they are a chip / hardware maker first. And even for them 50B training run with no exponential gains seems unreasonable. Better to optimize the architecture / approach first, which also is what most companies are doing now before scaling out.

mdp2021 2y ago

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 2y ago
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.

Jensson 2y ago

> You're just taking the translation to concepts that the LLM is certainly already doing and trying to make it explicitly forced. That is what tokens are doing in the first place though, and you get better results with tokens instead of letters.

mdp2021 2y ago
Well, individual letters in these languages in use* do not convey specific meaning, while individual tokens do - so, you cannot really construe a ladder that would go from letter to token, then from token to sentence. This said, to research whether the search for concepts (in the solutions space) works better than the search for tokens seems absolutely dutiful, in absence of a solid theory that showed otherwise. (*Sounds convey their own meaning e.g. in proto-Indo-European according to some interpretations, but that becomes too remote in the current descendants - you cannot reconstruct the implicit sound-token in words directly in English, just from the spelling.)
IanCal 2y ago
Is that true? I thought there was a desire to move towards byte level work rather than tokens, and that the benefits of tokens was more that you are reducing the context size for the same input.
- fngjdflmdflg 2y ago
  >there was a desire to move towards byte level work rather than tokens Yeah, latest work on this is from Meta a last month.[0] It showed good results. [0] https://ai.meta.com/research/publications/byte-latent-transformer-patches-scale-better-than-tokens/ https://ai.meta.com/research/publications/byte-latent-transf... (https://news.ycombinator.com/item?id=42415122 https://news.ycombinator.com/item?id=42415122)

blurbleblurble 2y ago

At a performance boost of 10-100x :)