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Counting (on) large language models

Jones, Max and Ladyman, James and Nefdt, Ryan M. (2026) Counting (on) large language models. [Preprint]

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Abstract

As large language models (LLMs) such as ChatGPT, Claude, Gemini, and Perplexity become increasingly
ubiquitous as both tools and objects of scientific study, in addition to their established roles as chatbots,
text generators and translators, questions about their identity conditions become scientifically as well as
philosophically and socially important. This paper is about how to count language models. We argue that
much of the emerging literature on these systems presupposes an answer to the question of identity for
these AIs but lacks a framework for counting or individuating them. We propose four levels of analysis,
inspired by an analogy with software as well as insights from social epistemology. This account produces
practical consequences for both research on language models, the ethics of AI, and our everyday talk about
them.

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Item Type: Preprint
Creators:
CreatorsEmailORCID
Jones, Maxmax.jones@bristol.ac.uk
Ladyman, Jamesjames.ladyman@bristol.ac.uk
Nefdt, Ryan M.ryan.nefdt@uct.ac.za0000-0002-2118-9960
Keywords: LLMs, chatbots, AI
Subjects: Specific Sciences > Artificial Intelligence > AI and Ethics
Specific Sciences > Artificial Intelligence
Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User: James Ladyman
Date Deposited: 24 Jan 2026 19:35
Last Modified: 24 Jan 2026 19:35
Item ID: 27985
Subjects: Specific Sciences > Artificial Intelligence > AI and Ethics
Specific Sciences > Artificial Intelligence
Specific Sciences > Artificial Intelligence > Machine Learning
Date: 23 January 2026
URI: https://philsci-archive.pitt.edu/id/eprint/27985

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