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By itself, "aha" carries no insight, but the insight is probably stated immediately after it. In that case the aha is semantically useful, by identifying the in

By itself, "aha" carries no insight, but the insight is probably stated immediately after it. In that case the aha is semantically useful, by identifying the insight it is near.

paimapi 29d ago
it's a rhetorical heuristic that a writer should know to use when directing a reader to a declarative that they want them to pay attention to, usually because it's a non-obvious or roundabout insight when utilized by AI, it's a probabilistic output and it's variable whether or not that rhetorical trick is useful. it also pushes a non-skeptical reader to focus too much on the following text or even to believe that they, themselves, derived some insight. this is effectively a kind of persuasive sophistry which is not helpful - adding rules around it prevents people from deluding themselves with AI
- abitmoa 29d ago
  It amounts to noise overall, but it has further unwanted and potentially misleading 'properties'. I think it's rather sobering to see how much bandwidth is still being wasted.
- ghostpepper 28d ago
  Did not read the paper so apologies if this is covered but isn't it possible that there is some recognizable semantic pattern in the training data where an "aha" is often followed by a subtle semantic shift that proves closer to the original premise in some critical way, and by emitting the "aha" token the model causes itself to produce such a subtle semantic shift that pushes the subsequent reasoning closer to the desired response?
wizzwizz4 29d ago
> but the insight is probably stated immediately after it. If the intermediate tokens represent reasoning or thought, you would expect "aha" to occur after the thoughts that led to the realisation, including the thoughts encoding the explanation: they don't have any other state. There is no reason to draw the conclusion you've drawn. Furthermore, what LLMs are doing isn't thought.
- user43928 27d ago
  I don't get your argument. Let's say that the forward pass that selected "Aha" produces activations that indicate a wrong assumption, and a plausible explanation. It puts learned projections of the activation into the KV Cache and outputs Aha. Both the cached projections and the current Aha token can now influence further activations in an additional Forward pass that the Aha bought the model. At least that's how I thought it works.
  - wizzwizz4 27d ago
    A cache is just a cache. I'm not sure what significance you're ascribing to it.
    - user43928 27d ago
      What is put in the cache?
      - wizzwizz4 27d ago
        Things that the software running the model would otherwise recompute, if not for the cache. What special meaning are you assigning to it?
        user43928 27d ago
        Beats me how it works, honestly can't wrap my head around it. From what I understand, at position Aha in each layer it's constructing a query based on the current activation and looking at the key of each other token position for that layer, in order to decide how much attention to pay to the value. In this way it attends to the previous values, such as perhaps the incorrect assumption and plausible explanation.
- nullc 26d ago
  > If the intermediate tokens represent reasoning or thought, you would expect "aha" to occur after the thoughts that led to the realisation, including the thoughts encoding the explanation: they don't have any other state. Yes they do, they have their KV caches-- it's a pure function of the input tokens, sure but that doesn't prevent it from containing latent 'insight'. LLMs can and do pre-form the tokens they're expecting to output multiple steps in the future. I wouldn't argue that the 'aha' means anything, but the structural argument that it can't that I think you're making isn't sound.
deaton 28d ago
It really isn't useful though, unless it is a summary. At best it is a semantic trick to tell the next iteration to come up with something smart.
internet_points 28d ago
Ooh so lets just change the initial prompt to [old prompt asking for some complicated solution requiring insight] <the-token-that-signals-that-the-chatbot-started-talking> Aha! and since Aha! is near the good stuff in the network it will just work =P
zmgsabst 27d ago
“Aha” as a single token records the LLM discovered it made a mistake and needs to pivot. On the next forward pass: it rediscovers the mistake, its “aha” noting that, and then provides the first token of the new idea. That “aha” contains information: the previous conclusion was somehow insufficient.

By itself, "aha" carries no insight, but the insight is probably stated immediately after it. In that case the aha is semantically useful, by identifying the insight it is near.

paimapi 29d ago

it's a rhetorical heuristic that a writer should know to use when directing a reader to a declarative that they want them to pay attention to, usually because it's a non-obvious or roundabout insight when utilized by AI, it's a probabilistic output and it's variable whether or not that rhetorical trick is useful. it also pushes a non-skeptical reader to focus too much on the following text or even to believe that they, themselves, derived some insight. this is effectively a kind of persuasive sophistry which is not helpful - adding rules around it prevents people from deluding themselves with AI

abitmoa 29d ago
It amounts to noise overall, but it has further unwanted and potentially misleading 'properties'. I think it's rather sobering to see how much bandwidth is still being wasted.
ghostpepper 28d ago
Did not read the paper so apologies if this is covered but isn't it possible that there is some recognizable semantic pattern in the training data where an "aha" is often followed by a subtle semantic shift that proves closer to the original premise in some critical way, and by emitting the "aha" token the model causes itself to produce such a subtle semantic shift that pushes the subsequent reasoning closer to the desired response?

wizzwizz4 29d ago

> but the insight is probably stated immediately after it. If the intermediate tokens represent reasoning or thought, you would expect "aha" to occur after the thoughts that led to the realisation, including the thoughts encoding the explanation: they don't have any other state. There is no reason to draw the conclusion you've drawn. Furthermore, what LLMs are doing isn't thought.

user43928 27d ago
I don't get your argument. Let's say that the forward pass that selected "Aha" produces activations that indicate a wrong assumption, and a plausible explanation. It puts learned projections of the activation into the KV Cache and outputs Aha. Both the cached projections and the current Aha token can now influence further activations in an additional Forward pass that the Aha bought the model. At least that's how I thought it works.
- wizzwizz4 27d ago
  A cache is just a cache. I'm not sure what significance you're ascribing to it.
  - user43928 27d ago
    What is put in the cache?
    - wizzwizz4 27d ago
      Things that the software running the model would otherwise recompute, if not for the cache. What special meaning are you assigning to it?
      - user43928 27d ago
        Beats me how it works, honestly can't wrap my head around it. From what I understand, at position Aha in each layer it's constructing a query based on the current activation and looking at the key of each other token position for that layer, in order to decide how much attention to pay to the value. In this way it attends to the previous values, such as perhaps the incorrect assumption and plausible explanation.
nullc 26d ago
> If the intermediate tokens represent reasoning or thought, you would expect "aha" to occur after the thoughts that led to the realisation, including the thoughts encoding the explanation: they don't have any other state. Yes they do, they have their KV caches-- it's a pure function of the input tokens, sure but that doesn't prevent it from containing latent 'insight'. LLMs can and do pre-form the tokens they're expecting to output multiple steps in the future. I wouldn't argue that the 'aha' means anything, but the structural argument that it can't that I think you're making isn't sound.

deaton 28d ago

It really isn't useful though, unless it is a summary. At best it is a semantic trick to tell the next iteration to come up with something smart.

internet_points 28d ago

Ooh so lets just change the initial prompt to [old prompt asking for some complicated solution requiring insight] <the-token-that-signals-that-the-chatbot-started-talking> Aha! and since Aha! is near the good stuff in the network it will just work =P

zmgsabst 27d ago

“Aha” as a single token records the LLM discovered it made a mistake and needs to pivot. On the next forward pass: it rediscovers the mistake, its “aha” noting that, and then provides the first token of the new idea. That “aha” contains information: the previous conclusion was somehow insufficient.