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Wouldn't this apply to all prediction machines that make errors. Humans make bad predictions all the time but we still seem to manage to do some cool stuff her

Wouldn't this apply to all prediction machines that make errors. Humans make bad predictions all the time but we still seem to manage to do some cool stuff here and there. part of an agents architecture will be for it to minimize e and then ground the prediction loop against a reality check. making LLMs bigger gets you a lower e with scale of data and compute but you will still need it to check against reality. test time compute also will play a roll as it can run through multiple scenarios and "search" for an answer.

throwawaymaths 2y ago
No. Many prediction machines can give you a confidence value on the full outcome. By the nature of tokenization and the casual inference (you build a token one at a time, and they're not really semantically connected except in the kv cache lookups, which are generally hidden to the user), the confidence values are thrown out in practice and even a weak confidence value would be hard to retrieve. I don't think it's impossible to obtain content with confidence assessments with the transformer architecture but maybe not in the way it's done now (like maybe another mayer on top).
- deleted 2y ago
  [deleted]
YeGoblynQueenne 2y ago
The difference between LLMs and other kinds of predictive models, or humans, is that those kinds of systems do not produce their output one token at a time, but all in one go, so their error basically stays constant. LeCun's argument is that LLM error increases with every cycle of appending a token to the last cycle's output. That's very specific to LLMs (or, well, to LLM-based chatbots to be more precise). >> part of an agents architecture will be for it to minimize e and then ground the prediction loop against a reality check. The problem is that web-scale LLMs can only realistically be trained to maximise the probability of the next token in a sequence, but not the factuality, correctness, truthfullness, etc of the entire sequence. That's because web-scale data is not annotated with such properties. So they can't do a "reality check" because they don't know what "reality" is, only what text looks like. The paper above uses an "oracle" instead, meaning they have a labelled dataset of correct answers. They can only train their RL approach because they have this source of truth. This kind of approach just doesn't scale as well as predicting the next token. It's really a supervised learning approach hiding behind RL.
- psb217 2y ago
  "The difference between LLMs and other kinds of predictive models, or humans, is that those kinds of systems do not produce their output one token at a time, but all in one go, so their error basically stays constant." -- This is a big, unproven assumption. Any non-autoregressive model can be trivially converted to an autoregressive model by: (i) generating a full output sequence, (ii) removing all tokens except the first one, (iii) generating a full-1 output sequence conditioned on the first token. This wraps the non-autoregressive model in an "MPC loop", thereby converting it to an autoregressive model where per-token error is no greater than that of the wrapped non-AR model. The explicit MPC planning behavior might reduce error per token compared to current naive applications of AR transformers, but the MPC-wrappped model is still an AR model, so the problem is not AR per se. LeCun's argument has some decent points, eg, allocating compute per token based solely on location within the sequence (due to increasing cost of attention ops for later locations) is indeed silly. However, the points about AR being unavoidably flawed due to exponential divergence from the true manifold are wrong and lazy. They're not wrong because AR models don't diverge, they're wrong because this sort of divergence is also present in other models.
  - pptr 2y ago
    The loop itself is claimed to be the problem. It doesn't matter whether you use an AR or non-AR model. They both have a certain error probability that gets amplified in each iteration.
    - psb217 2y ago
      The per token error of the non-AR model wrapped with MPC is no higher than the per token error of the non-AR model without MPC. Likelihood of the entire sequence being off the true data manifold is just one minus the product of the per token errors, whether or not you're running with the MPC loop. Ie, wrapping the non-AR model in an MPC loop and thereby converting it to an AR model (with a built-in planning mechanism) doesn't increase its probability of going off track. Per token error compounding over sequence length happens whether or not the model's autoregressive. The way in which per token errors correlate across a sequence might be more favorable wrt probability of producing bad sequences if you incorporate some explicit planning mechanism -- like the non-AR model wrapped in an MPC loop, but that's a more subtle argument than LeCun makes.
slashdave 2y ago
Humans self-correct (they can push the delete button)

throwawaymaths 2y ago

No. Many prediction machines can give you a confidence value on the full outcome. By the nature of tokenization and the casual inference (you build a token one at a time, and they're not really semantically connected except in the kv cache lookups, which are generally hidden to the user), the confidence values are thrown out in practice and even a weak confidence value would be hard to retrieve. I don't think it's impossible to obtain content with confidence assessments with the transformer architecture but maybe not in the way it's done now (like maybe another mayer on top).

deleted 2y ago
[deleted]

YeGoblynQueenne 2y ago

The difference between LLMs and other kinds of predictive models, or humans, is that those kinds of systems do not produce their output one token at a time, but all in one go, so their error basically stays constant. LeCun's argument is that LLM error increases with every cycle of appending a token to the last cycle's output. That's very specific to LLMs (or, well, to LLM-based chatbots to be more precise). >> part of an agents architecture will be for it to minimize e and then ground the prediction loop against a reality check. The problem is that web-scale LLMs can only realistically be trained to maximise the probability of the next token in a sequence, but not the factuality, correctness, truthfullness, etc of the entire sequence. That's because web-scale data is not annotated with such properties. So they can't do a "reality check" because they don't know what "reality" is, only what text looks like. The paper above uses an "oracle" instead, meaning they have a labelled dataset of correct answers. They can only train their RL approach because they have this source of truth. This kind of approach just doesn't scale as well as predicting the next token. It's really a supervised learning approach hiding behind RL.

psb217 2y ago
"The difference between LLMs and other kinds of predictive models, or humans, is that those kinds of systems do not produce their output one token at a time, but all in one go, so their error basically stays constant." -- This is a big, unproven assumption. Any non-autoregressive model can be trivially converted to an autoregressive model by: (i) generating a full output sequence, (ii) removing all tokens except the first one, (iii) generating a full-1 output sequence conditioned on the first token. This wraps the non-autoregressive model in an "MPC loop", thereby converting it to an autoregressive model where per-token error is no greater than that of the wrapped non-AR model. The explicit MPC planning behavior might reduce error per token compared to current naive applications of AR transformers, but the MPC-wrappped model is still an AR model, so the problem is not AR per se. LeCun's argument has some decent points, eg, allocating compute per token based solely on location within the sequence (due to increasing cost of attention ops for later locations) is indeed silly. However, the points about AR being unavoidably flawed due to exponential divergence from the true manifold are wrong and lazy. They're not wrong because AR models don't diverge, they're wrong because this sort of divergence is also present in other models.
- pptr 2y ago
  The loop itself is claimed to be the problem. It doesn't matter whether you use an AR or non-AR model. They both have a certain error probability that gets amplified in each iteration.
  - psb217 2y ago
    The per token error of the non-AR model wrapped with MPC is no higher than the per token error of the non-AR model without MPC. Likelihood of the entire sequence being off the true data manifold is just one minus the product of the per token errors, whether or not you're running with the MPC loop. Ie, wrapping the non-AR model in an MPC loop and thereby converting it to an AR model (with a built-in planning mechanism) doesn't increase its probability of going off track. Per token error compounding over sequence length happens whether or not the model's autoregressive. The way in which per token errors correlate across a sequence might be more favorable wrt probability of producing bad sequences if you incorporate some explicit planning mechanism -- like the non-AR model wrapped in an MPC loop, but that's a more subtle argument than LeCun makes.

slashdave 2y ago

Humans self-correct (they can push the delete button)