AI 与机器学习2024arxiv.org
大型模型是什么的模型？把工程成就误认为语言能动性
Large models of what? Mistaking engineering achievements for linguistic agency
进阶 · 约 35 分钟读完前置知识：大语言模型基础、语言学基础、具身认知
导读
这篇论文追问大语言模型究竟“大”在什么意义上，认为当前模型展示的是工程成就，而非真正的语言能动性。作者可能从语言完整性、数据完整性和具身经验等假设出发，论证把 LLM 的语言表现等同于语言主体性是一种误认。
HN 关注点：LLM 是否只是工程成就而非真正的语言理解，以及论文的论证是否足够有力、是否只是重复已有立场。
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这几条评论里：质疑 4
质疑同意结论但认为论证循环、说服力不足，尤其具身性和多模态反驳未被处理。
我对 LLM 通向 AGI 高度怀疑，但觉得这篇论文也相当缺乏说服力，近乎同义反复。我同意它的结论，但如果我原本不同意，它不会说服我。关于具身性：LLM 没有身体经验，但这不显然意味着它们先验地不能有“内部”现实概念，也不能从文本中获得理解；论证有循环之嫌。多模态模型能看能听，也让“只看到文本”的论点变弱。所谓行为不像人（比如不要求澄清）只说明目前不匹配，不说明永远不能。
GeneralMayhem查看英文原文
I am highly skeptical of LLMs as a mechanism to achieve AGI, but I also find this paper fairly unconvincing, bordering on tautological. I feel similarly about this as to what I've read of Chalmers - I agree with pretty much all of the conclusions, but I don't feel like the text would convince me of those conclusions if I disagreed; it's more like it's showing me ways of explaining or illustrating what I already believed. On embodiment - yes, LLMs do not have corporeal experience. But it's not obvious that this means that they cannot, a priori, have an "internal" concept of reality, or that it's impossible to gain such an understanding from text. The argument feels circular: LLMs are similar to a fake "video game" world because they aren't real people - therefore, it's wrong to think that they could be real people? And the other half of the argument is that because LLMs can only see text, they're missing out on the wider world of non-textual communication; but then, does that mean that human writing is not "real" language? This argument feels especially weak in the face of multi-modal models that are in fact able to "see" and "hear". The other flavor of argument here is that LLM behavior is empirically non-human - e.g., the argument about not asking for clarification. But that only means that they aren't currently matching humans, not that they couldn't. Basically all of these arguments feel like they fall down to the strongest counterargument I see proposed by LLM-believers, which is that sufficiently advanced mimicry is not only indistinguishable from the real thing, but at the limit in fact is the real thing. If we say that it's impossible to have true language skills without implicitly having a representation of self and environment, and then we see an entity with what appears to be true language skills, we should conclude that that entity must contain within it a representation of self and environment. That argument doesn't rely on any assumptions about the mechanism of representation other than a reliance on physicalism. Looking at it from the other direction, if you assume that all that it means to "be human" is encapsulated in the entropy of a human body, then that concept is necessarily describable with finite entropy. Therefore, by extension, there must be some number of parameters and some model architecture that completely encode that entropy. Questions like whether LLMs are the perfect architecture or whether the number of parameters required is a number that can be practically stored on human-manufacturable media are engineering questions, not philosophical ones: finite problems admit finite solutions, full stop. Again, that conclusion feels wrong to me... but if I'm being honest with myself, I can't point to why, other than to point at some form of dualism or spirituality as the escape hatch.
质疑质疑 LLM 与生物意识之间只有隐喻关系，缺乏经验证据。
我是外行，但一直有个疑问：有经验证据表明 LLM 或其底层机器学习技术与生物意识有任何共同点吗，还是说“神经网络”只是从神经科学借来的方便隐喻？仅仅因为受大脑结构启发或模仿其基本元素，并不意味着它和所建模的现实有字面关系，更不足以产生我们知之甚少的意识现象。我们连远比大脑简单的生物组织都还没复现，难道要直接跳到智能心智？
nativeit查看英文原文
I'm more or less a layperson when it comes to LLMs and this nascent concept of AI, but there's one argument that I keep seeing that I feel like I understand, even without a thorough fluency with the underlying technology. I know that neural nets, and the mechanisms LLMs employ to train and form relational connections, can plausibly be compared to how synapses form signal paths between neurons. I can see how that makes intuitive sense. I'm struggling to articulate my cognitive dissonance here, but is there any empirical evidence that LLMs, or their underlying machine learning technology, share anything at all with biological consciousness beyond a convenient metaphor for describing "neural networks" using terms borrowed from neuroscience? I don't know that it necessarily follows that just because something was inspired by, or is somehow mimicking, the structure of the brain and its basic elements, that it should necessarily relate to its modeled reality in any literal way, let alone provide a sufficient basis for instantiating a phenomena we frankly know very little about. Not for nothing, but our models naturally cannot replicate any biological functions we do not fully understand. We haven't managed to reproduce biological tissues that are exponentially less complex than the brain, are we really claiming that we're just jumping straight past lab-grown t-bones to intelligent minds? I'm sure most of the people reading this will have seen Matt Parker's videos where they "teach" matchbooks to win a game against humans. Is anyone suggesting those matchbooks, given infinite time and repetition, would eventually spark emergent consciousness? > The argument would be that that conceptual model is encoded in the intermediate-layer parameters of the model, in a different but analogous way to how it's encoded in the graph and chemical structure of your neurons. Sorry if I have misinterpreted anyone. I honestly thought all the "neuron" and "synapse" references were handy metaphors to explain otherwise complex computations that resemble this conceptual idea of how our brains work. But it reads a lot like some of the folks in this thread believe it's much more than metaphors, but rather a literal analog.
质疑批评作者借热点，并指出语言/数据完整性论证站不住脚。
这篇论文的作者不过是又一个借 AI 热度吸引注意、其实与 AI 没什么关系的人：“这是我们对当前热点的看法，请阅读并多多引用。”文中说语言完整性假设某种完整自然语言存在、可被 LLM 有效全面建模——把“LLM”换成“语言学”也一样。数据完整性假设语言可被数据完全量化捕捉——但婴儿成为母语者时也只有有限数据；语言习得并不要求数据 totality，母语者的词汇和语法规则也各不相同。
kazinator查看英文原文
The authors of this paper are just another instance of the AI hype being used by people who have no connection to it, to attract some kind of attention. "Here is what we think about this current hot topic; please read our stuff and cite generously ..." > Language completeness assumes that a distinct and complete thing such as `a natural language' exists, the essential characteristics of which can be effectively and comprehensively modelled by an LLM Replace "LLM" by "linguistics". Same thing. > The assumption of data completeness relies on the belief that a language can be quantified and wholly captured by data. That's all that a baby has, who becomes a native speaker of their surrounding language. Language acquisition does not imply totality of data. Not every native speaker recognizes exactly the same vocabulary and exactly the same set of grammar rules.
质疑质疑作者对 LLM 的实际经验不足，举例过时且忽略了嵌套语境能力。
他们想了这么多关于 LLM 的东西，但实际试过多少？我有很长的对话线程让 ChatGPT 反复改进代码问题的解法。他们举的“打印 10 个哲学家的小列表后就失去线索”的例子显得很过时。而且 LLM 似乎也会使用嵌套语境，比如在讲故事或说假设情况时能打破自己的规则。
KHRZ查看英文原文
That's a lot of thinking they've done about LLMs, but how much did they actually try LLMs? I have long threads where ChatGPT refine solutions to coding problems. Their example of losing the thread after printing a tiny list of 10 philosophers seems really outdated. Also it seems LLMs utilize nested contexts as well, for example when it can break it' own rules while telling a story or speaking hypothetically.
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