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> While a human may say “aha” to indicate exactly a sudden internal state change, this interpretation is unwarranted for models which do not have any such inter
by florianherrengt 2mo ago
> While a human may say “aha” to indicate exactly a sudden internal state change, this interpretation is unwarranted for models which do not have any such internal state, and which on the next forward pass will only differ from the pre-aha pass by the inclusion of that single token in their context. Interpreting the “aha” moment as meaningful exemplifies the long-neglected assumption about long CoT models – the false idea that derivational traces are semantically meaningful, either in resemblance to algorithm traces or to human reasoning.
This paper addresses something that has always bothered me about LLMs. You read their reasoning, see something like “Wait, that’s wrong” and then watch them make the exact mistake they just identified.
- Jeff_Brown 2mo agoBy 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 2mo agoit'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 2mo agoIt 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 2mo agoDid 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 2mo 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 2mo agoI 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 2mo agoA cache is just a cache. I'm not sure what significance you're ascribing to it.
- user43928 2mo agoWhat is put in the cache?
- wizzwizz4 2mo agoThings that the software running the model would otherwise recompute, if not for the cache. What special meaning are you assigning to it?
- user43928 2mo agoBeats 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.
- deaton 2mo agoIt 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 2mo agoOoh 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 2mo 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.