mlsanityData to 14 Oct 2026

Do Language Models' Words Refer?

arXiv 2308.05576 ↗First posted 10 Aug 2023cs.CLcited by 17

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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.

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

3 of 28 references.

References · 3

  1. 2019Visualizing and Measuring the Geometry of BERTcited by 216
  2. 2021Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Sensescited by 39
  3. 2022Entailment Semantics Can Be Extracted from an Ideal Language Modelcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 5; 2026: 5.

17 papers.

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  1. 2024Standards for Belief Representations in LLMscited by 30
  2. 2024A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debatescited by 23
  3. 2024Language 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 Referencecited by 15
  4. 2024Large language models and linguistic intentionalitycited by 6
  5. 2023Meaning and understanding in large language modelscited by 5
  6. 2025Large Language Model probabilities cannot distinguish between possible and impossible languagecited by 5
  7. 2024No Such Thing as a General Learner: Language models and their dual optimizationcited by 1
  8. 2025Cooperative Speech, Semantic Competence, and AIcited by 1
  9. 2025For GPT-4 as with Humans: Information Structure Predicts Acceptability of Long-Distance Dependenciescited by 1
  10. 2025The Outputs of Large Language Models are Meaninglesscited by 1
  11. 2026Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Powercited by 1
  12. 2024CV-Probes: Studying the interplay of lexical and world knowledge in visually grounded verb understandingcited by 0
5 more
  1. 2025A framework for auditing grounding claimscited by 0
  2. 2026Chatbots Output Meaningful (but Problematic) Languagecited by 0
  3. 2026Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Modelscited by 0
  4. 2026A Formal Limitation on Learning Human Language From Textual Corporacited by 0
  5. 2026A Polyphonic Conception of AI Understandingcited by 0