Chatbots Output Meaningful (but Problematic) Language
Quick Take
The paper argues that AI chatbots like Anthropic's Claude produce meaningful language without requiring anthropomorphic assumptions about mental states. It challenges dominant theories by suggesting that current models can be understood through existing human language theories, emphasizing that meaningful output does not equate to endorsement of the content. This perspective has significant implications for how we engage with both human and AI-generated text.
Key Points
- AI chatbots' outputs are deemed meaningful without needing mental state assumptions.
- The paper critiques dominant intentionalist accounts of language and meaning.
- It advocates for a 'de-anthropomorphization' of language understanding.
- Meaningful outputs do not imply endorsement of the chatbot's content.
- The findings impact theories of human linguistic output and AI text.
Article Content
From source RSS / original summaryarXiv:2606. 02973v1 Announce Type: new 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.
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