by Ali Minai
But nature is a stranger yet:
The ones that cite her most
Have never passed her haunted house,
Nor simplified her ghost.
—Emily Dickinson, “What Mystery Pervades a Well”
The first article of this 2-part series laid out the idea of emergence in complex systems, discussed how the appearance of abilities such as the generation of grammatical, syntactically correct, and meaningful text can reasonably be seen as an example of emergence, but also why these emergent abilities are just a shadow – or ghost – of the deeper language generation process in humans. This second part gets more deeply into the last point, making a detailed argument for why the linguistic abilities of LLMs should be seen as limited, and what would be needed to extend them.
The Meaning of Meaning
Perhaps the most critical difference between an LLM’s model of language and that of a human is in the way meanings are understood in the two. The task on which LLMs are trained provides no explicit information about meanings, and depends only on knowing the structural relationships between words in text. However, the fact that LLMs almost always use words in meaningful ways indicates that they have an implicit model of meanings too. What is the nature of that? The answer lies almost certainly in a linguistic idea called the distributional hypothesis of meaning, which says that the meaning of a word can be inferred from the statistics of its use in the context of other words. As described above, LLMs based on transformers are pre-disposed to the statistical learning of structural relationships in text, and their representations of meaning must be derived implicitly from this because of the tight linkage between word usage and meaning per the distributional hypothesis. Given enough data, the statistics can become very accurate – hence the meaningful output of GPT-4 et al. But such meanings – though accurate for purposes to usage – are abstractions. In contrast, the meanings in the human mind are grounded in experience. GPT-4 might understand the meaning of the word “burn” as something associated with the words, “fire”, “heat”, “flame”, “smoke”, “pain”, etc., but a person understands it in terms of the sensation of feeling heat and getting burned. Similarly, GPT-4 may use the words “near” and “far” correctly, but it has no experience of an object being near enough to touch or too far away to recognize. Concrete meanings in the human mind are thus grounded in the fact of the human animal being embodied – a physical system with sensations and the capability of action that leads to physical consequences. The LLM is “all talk” – just simulating concrete meanings as ungrounded symbols. Of course, not all concepts in human language are concrete enough to be defined in direct experiential terms, and there is ongoing debate about how the mind grounds abstract meanings. However, it seems plausible that they are grounded in the substrate of more concrete meanings, and thus indirectly in experience as well (see reference [1] for a recent overview, reference [2] for my views in detail). It is in this sense above all that LLM models are ghosts in the machine. Read more »