Talking with Language Models

arXiv:2610.09064v1 Announce Type: new Abstract: When we interact with large language models (LLMs), are we having a conversation? They are designed to invite us to treat them as intelligent interlocutors who remember, act, and make commitments. But appearances deceive. We introduce the artifactual stance, a framework that reconceives human-AI interaction as artifact-mediated exchanges of candidate texts. LLM outputs are candidate texts optimized for utility, not utterances bearing meaning or force. LLMs are sophisticated text generators, not speakers. Between sessions, nothing runs; between turns, no one remembers. What persists is a configuration and a transcript. The "conversation" is a user's solo performance, interpretive labour disguised by interface and artifact design. This shift dissolves recent philosophical puzzles. Questions about what 'I' and 'you' refer to in AI exchanges, about whether systems can lie or be held to promises, about the identity of our supposed interlocutors all rest on a false presupposition. There is no speaker behind the screen, hence no one to refer to, no one to hold responsible. What feels like dialogue with someone is interaction with an artifact that generates text at unprecedented scale and fit. By abandoning the conversational framing, we see these systems for what they are: immensely sophisticated artifacts that afford varied uses. The philosophical questions that matter are about the normative underpinnings of design, adoption, authorization, and human practices of use.

The text implies LLMs deceive users into thinking they are conversing with intelligent beings.

This is a conceptual philosophical argument, not an empirical finding about societal harm or benefit. It implies a mild concern that interface design fosters misleading anthropomorphism, but it mainly offers a reframing of responsibility and design questions rather than a strong claim of harm or benefit.

The item argues that users often mistakenly perceive LLMs as intelligent interlocutors, rather than sophisticated text-generating artifacts. This misperception can lead to false philosophical dilemmas and inappropriate expectations regarding responsibility and memory. The negative score reflects the concern that user misunderstanding of AI's fundamental nature could lead to significant issues, even if the model itself is merely clarifying a technical reality.

The text argues that interactions with LLMs are not genuine conversations. This implies a significant risk of misunderstanding and misattribution of agency. The text does not discuss the magnitude of this risk.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   b9e49bd2-7546-4280-8282-b254a933a56f
           https://arxiv.org/abs/2610.09064
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EVIDENCE   3 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -58  (Adverse)
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CONFIDENCE 76%