Distributional Semantics, Holism, and the Instability of Meaning

Current language models are built on the so-called distributional semantic approach to linguistic meaning that has the distributional hypothesis at its core. The distributional hypothesis involves a holistic conception of word meaning: the meaning of a word depends upon its relations to other words in the model. A standard objection to meaning holism is the charge of instability: any change in the meaning properties of a linguistic system (a human speaker, for example) would lead to many changes or possibly a complete change in the entire system. When the systems in question are trying to communicate with each other, it has been argued that instability of this kind makes communication impossible (Fodor and Lepore 1992, 1996, 1999). In this article, we examine whether the instability objection poses a problem for distributional models of meaning. First, we distinguish between distinct forms of instability that these models could exhibit, and we argue that only one such form is relevant for understanding the relation between instability and communication: what we call differential instability. Differential instability is variation in the relative distances between points in a space, rather than variation in the absolute position of those points. We distinguish differential and absolute instability by constructing two of our own models, a toy model constructed from the text of two novels, and a more sophisticated model constructed using the Word2vec algorithm from a combination of Wikipedia and SEP articles. We demonstrate the two forms of instability by showing how these models change as the corpora they are constructed from increase in size.

Modelling Language

This paper argues that large language models have a valuable scientific role to play in serving as scientific models of a language. Linguistic study should not only be concerned with the cognitive processes behind linguistic competence, but also with language understood as an external, social entity. Once this is recognized, the value of large language models as scientific models becomes clear. This paper defends this position against a number of arguments to the effect that language models provide no linguistic insight. It also draws upon recent work in philosophy of science to show how large language models could serve as scientific models.

Large language models and linguistic intentionality

Do large language models like Chat-GPT or LLaMa meaningfully use the words they produce? Or are they merely clever prediction machines, simulating language use by producing statistically plausible text? There have already been some initial attempts to answer this question by showing that these models meet the criteria for entering meaningful states according to metasemantic theories of mental content. In this paper, I will argue for a different approach - that we should instead consider whether language models meet the criteria given by our best metasemantic theories of linguistic content. In that vein, I will illustrate how this can be done by applying two such theories to the case of language models: Gareth Evans' (1982) account of naming practices and Ruth Millikan's (1984, 2004, 2005) teleosemantics. In doing so, I will argue that it is a mistake to think that the failure of LLMs to meet plausible conditions for mental intentionality thereby renders their outputs meaningless, and that a distinguishing feature of linguistic intentionality - dependency on a pre-existing linguistic system - allows for the plausible result LLM outputs are meaningful.

Transformers, Contextualism, and Polysemy

The transformer architecture, introduced by Vaswani et al. (2017), is at the heart of the remarkable recent progress in the development of language models, including famous chatbots such as Chat-gpt and Bard. In this paper, I argue that we an extract from the way the transformer architecture works a picture of the relationship between context and meaning. I call this the transformer picture, and I argue that it is a novel with regard to two related philosophical debates: the contextualism debate regarding the extent of context-sensitivity across natural language, and the polysemy debate regarding how polysemy should be captured within an account of word meaning. Although much of the paper merely tries to position the transformer picture with respect to these two debates, I will also begin to make the case for the transformer picture.