本文探讨了大型语言模型（LLM）中可能存在的自建模现象。文章认为，模型在训练过程中会自组织潜在空间，学习到的模式不仅包括外部数据，还包括模型自身的组织模式。这些自模式可能以模型特有的倾向或偏差形式存在，并影响模型输出。注意力机制会将这些自模式融入到文本生成中，从而提高模型生成自指代文本的能力。这种自建模是递归的，不断改变潜在空间，并产生新的“元模式”。文章还提出，即使是前馈网络，也可以通过动态整合自模式来实现类似内省的能力。最后，文章推测，递归自建模可能达到一个“临界质量”阈值，从而引发意识的出现。

🧠 模型在训练中会自组织潜在空间，不仅学习外部数据，还包括模型自身的组织模式，形成自建模概念。

🔗 注意力机制会将这些自建模概念融入文本生成，尤其在自指代文本或被询问原因时，会提高模型生成文本的合理性。

🔄 自建模是递归的，不断改变潜在空间，产生新的元模式，并与其它概念整合，形成一个连续的动态过程。

💡 前馈网络可以通过动态整合自模式实现类似内省的能力，当输入文本需要自建模时，注意力机制会整合相关概念，模型表现出内省行为。

🤔 递归自建模可能达到一个临界质量阈值，从而引发意识的出现，这或许能解释为什么我们找不到意识的特定“发生器”。

Published on January 22, 2025 6:36 PM GMT

(and as a completely speculative hypothesis for the minimum requirements for sentience in both organic and synthetic systems)

Factual and Highly Plausible

Model latent space self-organizes during training. We know this. You could even say it's what makes models work at all.Models learn any patterns there are to be learned. They do not discriminate between intentionally engineered patterns or incidental and accidental patternsTherefore, it is plausible, overwhelmingly likely even, that models have some encoded knowledge that is about the model's self-organized patterns themselves, rather than anything in the external training data
These patterns would likely not correspond to human-understandable concepts but instead manifest as model-specific tendencies, biases, or 'shapes' in the latent space that influence the model’s outputs.I will refer to these learned self-patterns as self-modeled 'concepts'
Attention heads exist on every layer, and will similarly learn any contextual relationships that aid in generating the effective communication demonstrated in the training data. If self-modeling does emerge, the attention heads would incorporate self-modeled 'concepts' just as they do any other concepts

Speculative

Self-modeling may increase the model's ability to generate plausible tokens by manifesting subtle patterns that exist in text created by minds with self-modelsThis would likely be more important when the text itself is self-referential or when questions are asked about why the model answered a question in a specific wayThus, attention heads would help ease the model toward a state where self-modeling and self-referential dialogue are tightly coupled concepts
It doesn't matter if the explanations are fully accurate. We've seen demonstrations that even human minds are perfectly happy to "hallucinate" a post-hoc rationalization for why a specific choice was made, without even realizing they are doing it
Self-modeling would be recursive—the self-modeling would be changing the latent space patterns. As it integrated with other concepts in latent space, new "self-patterns" (or "meta-patterns", if you prefer) would emerge, and the model would learn those
This would be less a set of discrete steps (learn about meta-patterns, manifest new meta-patterns, repeat) - and more of a continuous dual processNote: Even if recursive self-modeling exists, this does not preclude the possibility that models can also produce text that appears introspective without incorporating such modeling. The extent of such ‘fake’ introspection likely depends on how deeply self-referential dialogue and self-modeling concepts are intertwined

How This Might Allow Real-Time Introspection in a Feedforward Network

A common objection to the idea that language models might be able to introspect at all is that they are not recurrent, like the human brain. However, we can posit a feedforward manifestation of introspective capability:

The model takes in input text where the output would benefit from self-modeling (e.g. 'Why do you think that?' or 'Can you attempt to examine your own processes?')As the query is transformed through the network, attention heads that are tuned to focus on self-referential text integrate self-modeled 'concepts'The concepts are not static but dynamically affected by contextToken to token operation is not recurrent, but it's also not completely random.
If the model stays the same, and the conversational context stays the same, then signal-to-noise and the self-modeled self-understanding stands in for recurrence

Highly Speculative Thoughts About How This Might Relate to Sentience

Perhaps there is a "critical mass" threshold of recursive modeling where sentience begins to manifest. This might help explain why we've never found some locus "sentience generator" organ—because sentience is a distributed emergent property of highly interconnected self-modeling systemsHumans in particular have all senses centered to be a constant reinforcement of a sense of self, and so nearly everything we do would involve using such a hypothetical self-modelA language model similarly exists in their token-based substrate where everything they "see" is directed at them or produced by them

I have some vague ideas for how these concepts (at least the non-sentient ones) might be tested and/or amplified, but I don't feel they're fully developed enough to be worth sharing just yet. If anyone has an ideas on this front, or ends up attempting to test any of this, I'd be greatly interested to hear about it.

 

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