On the referential capacity of language models: An internalist rejoinder to Mandelkern & Linzen
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Abstract (arXiv)
In a recent paper, Mandelkern & Linzen (2024) - henceforth M&L - address the question of whether language models' (LMs) words refer. Their argument draws from the externalist tradition in philosophical semantics, which views reference as the capacity of words to "achieve 'word-to-world' connections". In the externalist framework, causally uninterrupted chains of usage, tracing every occurrence of a name back to its bearer, guarantee that, for example, 'Peano' refers to the individual Peano (Kripke 1980). This account is externalist both because words pick out referents 'out there' in the world, and because what determines reference are coordinated linguistic actions by members of a community, and not individual mental states. The "central question to ask", for M&L, is whether LMs too belong to human linguistic communities, such that words by LMs may also trace back causally to their bearers. Their answer is a cautious "yes": inputs to LMs are linguistic "forms with particular histories of referential use"; "those histories ground the referents of those forms"; any occurrence of 'Peano' in LM outputs is as causally connected to the individual Peano as any other occurrence of the same proper name in human speech or text; therefore, occurrences of 'Peano' in LM outputs refer to Peano. In this commentary, we first qualify M&L's claim as applying to a narrow class of natural language expressions. Thus qualified, their claim is valid, and we emphasise an additional motivation for that in Section 2. Next, we discuss the actual scope of their claim, and we suggest that the way they formulate it may lead to unwarranted generalisations about reference in LMs. Our critique is likewise applicable to other externalist accounts of LMs (e.g., Lederman & Mahowald 2024; Mollo & Milliere 2023). Lastly, we conclude with a comment on the status of LMs as members of human linguistic communities.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 0 of its 1 references that are in the map. First posted on arXiv 31 May 2024.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 3.
5 papers.
- 2025Large Language Model probabilities cannot distinguish between possible and impossible languagecited by 5
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- 2026Translation Indeterminacy and the Distributional Fallacycited by 0
- 2026Translation Indeterminacy and the Distributional Fallacy7 Sep 2026
- 2026A Formal Limitation on Learning Human Language From Textual Corpora28 Aug 2026
- 2026Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power22 Apr 2026
- 2025Large Language Model probabilities cannot distinguish between possible and impossible language18 Sep 2025
- 2024No Such Thing as a General Learner: Language models and their dual optimization18 Aug 2024