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Chatbots Output Meaningful (but Problematic) Language

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Computer Science > Computation and Language

arXiv:2606.02973 (cs)
[Submitted on 2 Jun 2026]

Title:Chatbots Output Meaningful (but Problematic) Language

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Abstract:Are utterances by AI chatbots meaningful? Concretely, if a user asks, say, Anthropic's agent Claude, "What is the capital of Spain?" and Claude answers, "Madrid is the capital of Spain," does that sentence have its ordinary meaning -- and does it express a true proposition? Most ordinary users, as well as AI engineers, take the answer to be trivially "yes." However, many cognitive scientists, linguists, and philosophers of language argue that dominant intentionalist accounts of language and meaning deliver the opposite conclusion.
Theorists more sympathetic to ordinary users' intuitions have therefore advocated a radical "de-anthropomorphization" of language, revising our understanding of mental states, intentions, and semantic content to capture the intuition that the outputs of LLMs are meaningful. We take a different approach. While we, too, argue that LLM outputs are meaningful, we contend that a proper theory of human language already applies, as is, to current chatbots. Meaning is a low bar: claiming that LLM outputs are meaningful does not require positing mental states, intentions, rationality, or the cognitive capacities requisite for communication in LLMs -- or, indeed, making any other anthropomorphic assumptions. People do have communicative intentions (typically successful ones), but nevertheless, even in humans, language production can depart from what the speaker has in mind.
Our view has important consequences for how we should theorize about -- and critically engage with -- both human linguistic output and synthetically generated text. In particular, to say that chatbots produce meaningful text is not by any means to endorse what they output, or to assume that the technology is (or is not) good, powerful, appropriate, or useful.
Comments: 49 pages
Subjects: Computation and Language (cs.CL)
ACM classes: I.2.0; I.2.7
Cite as: arXiv:2606.02973 [cs.CL]
  (or arXiv:2606.02973v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2606.02973
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Matthew Stone [view email]
[v1] Tue, 2 Jun 2026 00:24:42 UTC (72 KB)
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