Language as Mathematical Structure: Examining Semantic Field Theory Against Language Games
Not on the map. Why?
Abstract (arXiv)
Large language models (LLMs) offer a new empirical setting in which long-standing theories of linguistic meaning can be examined. This paper contrasts two broad approaches: social constructivist accounts associated with language games, and a mathematically oriented framework we call Semantic Field Theory. Building on earlier work by the author, we formalize the notions of lexical fields (Lexfelder) and linguistic fields (Lingofelder) as interacting structures in a continuous semantic space. We then analyze how core properties of transformer architectures-such as distributed representations, attention mechanisms, and geometric regularities in embedding spaces-relate to these concepts. We argue that the success of LLMs in capturing semantic regularities supports the view that language exhibits an underlying mathematical structure, while their persistent limitations in pragmatic reasoning and context sensitivity are consistent with the importance of social grounding emphasized in philosophical accounts of language use. On this basis, we suggest that mathematical structure and language games can be understood as complementary rather than competing perspectives. The resulting framework clarifies the scope and limits of purely statistical models of language and motivates new directions for theoretically informed AI architectures.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 1 Jan 2026.
Builds on
6 of 15 references.
References · 6
- 2017Attention Is All You Needcited by 59,932
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2019What Does BERT Look At? An Analysis of BERT's Attentioncited by 994
- 2023Learn2Extend: Extending sequences by retaining their statistical properties with mixture modelscited by 1
Built on by
Citing papers per year: 2026: 2.
2 papers.