# Do Language Models' Words Refer?

arXiv: https://arxiv.org/abs/2308.05576 · HTML: https://mlsanity.com/paper/2308.05576/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Visualizing and Measuring the Geometry of BERT](https://mlsanity.com/paper/1906.02715/) (2019)
- [Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Senses](https://mlsanity.com/paper/2104.14694/) (2021)
- [Entailment Semantics Can Be Extracted from an Ideal Language Model](https://mlsanity.com/paper/2209.12407/) (2022)

## Built on by (most cited first, 17 of 17)

- [Standards for Belief Representations in LLMs](https://mlsanity.com/paper/2405.21030/) (2024)
- [A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debates](https://mlsanity.com/paper/2401.03910/) (2024)
- [Language in Vivo vs. in Silico: Size Matters but Larger Language Models Still Do Not Comprehend Language on a Par with Humans Due to Impenetrable Semantic Reference](https://mlsanity.com/paper/2404.14883/) (2024)
- [Large language models and linguistic intentionality](https://mlsanity.com/paper/2404.09576/) (2024)
- [Meaning and understanding in large language models](https://mlsanity.com/paper/2310.17407/) (2023)
- [Large Language Model probabilities cannot distinguish between possible and impossible language](https://mlsanity.com/paper/2509.15114/) (2025)
- [No Such Thing as a General Learner: Language models and their dual optimization](https://mlsanity.com/paper/2408.09544/) (2024)
- [Cooperative Speech, Semantic Competence, and AI](https://mlsanity.com/paper/2504.16092/) (2025)
- [For GPT-4 as with Humans: Information Structure Predicts Acceptability of Long-Distance Dependencies](https://mlsanity.com/paper/2505.09005/) (2025)
- [The Outputs of Large Language Models are Meaningless](https://mlsanity.com/paper/2509.22206/) (2025)
- [Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power](https://mlsanity.com/paper/2604.21043/) (2026)
- [CV-Probes: Studying the interplay of lexical and world knowledge in visually grounded verb understanding](https://mlsanity.com/paper/2409.01389/) (2024)
- [A framework for auditing grounding claims](https://mlsanity.com/paper/2512.06205/) (2025)
- [Chatbots Output Meaningful (but Problematic) Language](https://mlsanity.com/paper/2606.02973/) (2026)
- [Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Models](https://mlsanity.com/paper/2608.14252/) (2026)
- [A Formal Limitation on Learning Human Language From Textual Corpora](https://mlsanity.com/paper/2608.28560/) (2026)
- [A Polyphonic Conception of AI Understanding](https://mlsanity.com/paper/2609.36079/) (2026)

## Abstract (arXiv)

What do language models (LMs) do with language? Everyone agrees that they can produce sequences of (mostly) coherent strings of English. But do those sentences mean something, or are LMs simply babbling in a convincing simulacrum of language use? Here we will address one aspect of this broad question: whether LMs' words can refer, that is, achieve "word-to-world" connections. There is prima facie reason to think they do not since LMs do not interact with the world in the way that ordinary language users do. Drawing on insights from the externalist tradition in philosophy of language, we argue that those appearances are misleading: even if the inputs to an LM are simply strings of text, they are strings of text with natural histories, and that may suffice to put LMs' words into referential contact with the external world.